=Paper=
{{Paper
|id=Vol-3630/paper38
|storemode=property
|title=Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain
|pdfUrl=https://ceur-ws.org/Vol-3630/LWDA2023-paper38.pdf
|volume=Vol-3630
|authors=Jonas Hanselle,Jaroslaw Kornowicz,Stefan Heid,Kirsten Thommes,Eyke Hüllermeier
|dblpUrl=https://dblp.org/rec/conf/lwa/HanselleKHTH23
}}
==Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain==
https://ceur-ws.org/Vol-3630/LWDA2023-paper38.pdf
Comparing Humans and Algorithms in Feature
Ranking: A Case-Study in the Medical Domain
Jonas Hanselle1,2,∗,† , Jaroslaw Kornowicz3,† , Stefan Heid3,† , Kirsten Thommes3 and
Eyke Hüllermeier1,2
1
LMU Munich, Germany
2
Munich Center for Machine Learning, Germany
3
Paderborn University, Germany
Abstract
The selection of useful, informative, and meaningful features is a key prerequisite for the successful
application of machine learning in practice, especially in knowledge-intense domains like decision
support. Here, the task of feature selection, or ranking features by importance, can, in principle, be
solved automatically in a data-driven way but also supported by expert knowledge. Besides, one may of
course, conceive a combined approach, in which a learning algorithm closely interacts with a human
expert. In any case, finding an optimal approach requires a basic understanding of human capabilities in
judging the importance of features compared to those of a learning algorithm. Hereto, we conducted a
case study in the medical domain, comparing feature rankings based on human judgment to rankings
automatically derived from data. The quality of a ranking is determined by the performance of a decision
list processing features in the order specified by the ranking, more specifically by so-called probabilistic
scoring systems.
Keywords
Feature Ranking, Feature Selection, Scoring System, Machine Learning, Decision Support
1. Introduction
With the increasing access to technology, computational resources, and massive amounts of data,
the idea of taking advantage of machine learning (ML) methodology to optimize decision support
is becoming more and more feasible. Automated or partially automated decision-making with
data-driven models is appealing for various reasons, especially as it is potentially more rational,
objective, and accurate than decision-making by humans alone, which may be subjective or
error-prone. For example, think of decisions in the context of employee recruitment, such
as hiring or placement decisions [1], or the construction of individualized treatment rules in
personalized medicine [2].
LWDA’23: Learning, Knowledge, Data, Analysis. October 09–11, 2023, Marburg, Germany
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open jonas.hanselle@ifi.lmu.de (J. Hanselle); jaroslaw.kornowicz@upb.de (J. Kornowicz); stefan.heid@upb.de
(S. Heid); kirsten.thommes@upb.de (K. Thommes); eyke@lmu.de (E. Hüllermeier)
Orcid 0000-0002-1231-4985 (J. Hanselle); 0000-0002-5654-9911 (J. Kornowicz); 0000-0002-9461-7372 (S. Heid);
0000-0002-8057-7162 (K. Thommes); 0000-0002-9944-4108 (E. Hüllermeier)
© 2023 Copyright by the paper’s authors. Copying permitted only for private and academic purposes. In: M. Leyer, Wichmann, J. (Eds.): Proceedings of the
LWDA 2023 Workshops: BIA, DB, IR, KDML and WM. Marburg, Germany, 09.-11. October 2023, published at http://ceur‐ws.org
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
That said, decision models constructed in a data-driven way will not be accepted by human
experts [3] — and hence not be used in practice — unless these models are comprehensible,
meaningful, and interpretable. In this regard, the selection and prioritization of decision criteria,
or features in machine learning jargon, appears to be of major importance: The features on
which a decision is based need to be semantically meaningful; features deemed relevant by the
expert should be included in the model, while irrelevant features should be omitted.
Needless to say, these properties are not necessarily guaranteed when selecting features in
a purely data-driven way. As another extreme, one may think of letting the human expert
preselect the features by hand. For various reasons, however, this might be suboptimal either,
for example, because the expert might be subjectively biased, or her knowledge might not
be perfect. Presumably, the best approach is somewhere in-between, namely, hybrid in the
sense that the human expert and the machine learning algorithm select features jointly in the
course of an interactive process. Either way, these considerations beg an essential question:
How capable are human experts in selecting the most important features or in ranking features
in descending order of importance, and how do human experts compare to ML algorithms
selecting features in a data-driven manner [4, 5, 6]?
This is the question addressed by the current paper. We conducted a case study in the medical
domain, comparing feature (importance) rankings based on human judgment to feature rankings
derived from data. The quality of a ranking is determined by the performance of a decision
list processing features in the order specified by the ranking. In a decision list, features are
considered incrementally, one by one. In each stage of the process, there are two options: either
a final decision is made based on the feature values seen so far, or the process is continued by
observing the next feature. Features should be ranked in decreasing order of importance to make
well-informed decisions as quickly as possible. We implement this approach with so-called
scoring systems, specifically appealing from an interpretability perspective and commonly used
in the medical domain [7, 8].
Previous research suggests that data-driven methods generally surpass knowledge-driven
methods in performance, though these findings are not entirely unambiguous. Our study
contributes to resolving this continuing debate and extends the current literature by assessing
these methods within the context of interpretable machine learning models. In high-stakes
environments such as in the medical domain, the constructor of the decision model can be
a significant factor for decision-makers, influencing their trust and reliance on the system.
Consequently, evaluating the quality of various feature selection methods on such models is
vital.
Our study shows that while data-driven feature ranking exhibits superior performance in
identifying patterns unseen by human actors, the risk of overfitting, especially in small or
biased datasets, necessitates the incorporation of human judgment for optimal results. We
suggest an interactive, co-constructive approach, merging human expertise with algorithmic
analytics, as a potential solution to offset overfitting effects while enhancing user acceptance of
decision models. We encourage future research to leverage our findings, specifically targeting
the inclusion of more domain professionals in the dataset, to further enrich and generalize these
insights across various fields.
2. Data- and Knowledge-Driven Feature Selection
In the realm of supervised machine learning, most algorithms assume a representation of data
objects (instances) in terms of feature vectors, which means that each object is specified by its
values on a predefined number of features, also known as independent variables, dimensions,
or inputs. The latter are supposed to carry important information for predicting the outcome
or target variable [9]. Careful feature selection is a crucial step in the modeling process and
a key prerequisite for learning accurate predictors [10]. Selecting a manageable number of
meaningful features also facilitates interpretability and explainability [6].
Feature selection has been researched intensively in the past, with a specific focus on data-
driven approaches. Here, an algorithm autonomously ranks or selects features based on the
properties of the data. In contrast, knowledge-driven approaches determine a feature subset
through literature review [11, 12, 13] or by consulting domain experts [4, 14]. Interactive
machine learning fosters a combination of these approaches [15]. For instance, experts might
underscore highly relevant observations and features that a data-driven algorithm can subse-
quently focus on [16]. Alternatively, experts might vote on different feature subsets, indirectly
revealing their subjective preferences [17]. It is also possible to aggregate multiple selection
and ranking methods into a single approach [18, 4, 19, 20].
Choosing the optimal method for a specific dataset and problem domain is inherently chal-
lenging. Guyon and Elisseeff [21] and Li et al. [6] advocate for including domain knowledge
in the selection process. Conversely, Filippova et al. [5] find human intervention to be less
beneficial than expected, while McKay [22] demonstrate that, for the same classification prob-
lem, a model with merely four features based on social science knowledge can rival models
involving 10,000 features. On the other side, Cheng et al. [4] find that the features chosen by
individual cardiologists, or an aggregation of their selections, can enhance accuracy compared to
a baseline of all features, although they are still outperformed by data-driven methods. In their
experimental study, Corrales et al. [11] observe that, in certain combinations of datasets and
learning algorithms, expert knowledge can outperform data-driven methods. They conclude
that expert knowledge can be especially beneficial under limited computational resources, for
example, when working with high-dimensional datasets.
3. Probabilistic Scoring Lists
A so-called scoring system is a simple decision model that checks a set of features, adds (or
subtracts) a certain number of points to a total score for each feature that is satisfied, and finally
makes a decision by comparing the total score to a threshold. Scoring systems have a long
history of active use in safety-critical domains such as healthcare [23] and justice [24], where
they provide guidance for making objective and accurate decisions.
Hanselle et al. [25] propose an extension of scoring systems, called probabilistic scoring
list (PSL). First, to increase uncertainty-awareness, a PSL produces predictions in the form
of probability distributions (instead of making deterministic decisions). Second, to increase
cost-efficiency, a PSL is conceptualized as a decision list: At prediction time, features are being
evaluated one by one. The procedure may be stopped as soon as the practitioner decides that the
confidence in the predictions is high enough for the application context at hand. In the example
in Table 1, the relevant information for an evaluation at stage 3 is highlighted in boldface. All
features with their accompanying scores up to that stage need to be evaluated. The probabilities
for the positive class are obtained by looking up the value corresponding to the total sum of
the selected scores 𝑇. Here, the task is to diagnose a patient as COVID-19 positive or negative,
given information about various features. In the concrete case, “Fatigue” would be determined
as a first feature, and if present, contributes a score of 2. Fever would then be determined as the
next feature, contributing a score of 1 if present, and this process continues with the remaining
features. At stage 2, the probability of the positive class is predicted as 0 if the total score is
0, 0.1 if the total score is 1, etc. Note that adding a feature with a corresponding score of 0 is
practically equivalent with ignoring said feature. Thus, we only consider score sets excluding 0.
Table 1
Example of a probabilistic scoring list for the COVID-19 use case
Stage Feature Score T=-1 T=0 T=1 T=2 T=3 T=4 T=5 T=6
0 - - - 0.1 - - - - - -
1 Fatigue +2 - 0.1 - 0.3 - - - -
2 Fever +1 - 0.0 0.1 0.2 0.4 - - -
3 Cough +2 - 0.0 0.1 0.1 0.2 0.2 0.5 -
4 Loss of smell +1 - 0.0 0.1 0.1 0.2 0.2 0.4 1.0
5 Contact w/ inf. person -1 0.0 0.0 0.1 0.1 0.2 0.4 0.4 1.0
The learning algorithm introduced in Hanselle et al. [25] constructs PSLs incrementally in
a greedy manner. Starting with the empty list, one additional feature with a corresponding
score (taken from a predefined set of scores) is added to the list in each stage. To this end, each
feature/score pair is tentatively added as a candidate, and the resulting model is evaluated in
terms of the expected entropy as performance measure:
𝑁𝑇
𝐸=∑ ̂ )) ,
⋅ 𝐻(𝑞(𝑇 (1)
𝑇 ∈Σ 𝑁
where Σ is the set of total scores that can be produced by the decision list, 𝑁 = |𝒟 | is the
total number of training examples, and 𝑁𝑇 the number of training examples with total score 𝑇.
̂ ) is the estimated probability of the positive class given total score 𝑇, and 𝐻 is the
Moreover, 𝑞(𝑇
Shannon entropy
𝐻 (𝑞) = −𝑞 ⋅ log(𝑞) − (1 − 𝑞) log(1 − 𝑞) .
The feature/score combination leading to the highest performance is eventually added to the
list, and the algorithm proceeds to the next stage (unless all features are used or the gain in
̂ ) are estimated in terms of relative
terms of expected entropy is negative). The probabilities 𝑞(𝑇
frequencies, rectified by isotonic regression to guarantee monotonicity (the probability of the
positive class increases with an increasing total score).
Note that the expected entropy (1) is a meaningful measure of informedness at every stage of
the decision process: The information provided by the prediction of a probability distribution 𝑞̂
is quantified in terms of Shannon entropy, which is an established measure of information, and
weighted by the (estimated) probability that this prediction is delivered.
The PSL produced by the above algorithm also suggests a ranking of features in the sense
that features appearing earlier in the list seem to be more important in terms of performance
than features queried only later on (or possibly not at all, if a decision is made before). With
a straightforward modification, the algorithm can also be used to learn scoring systems for a
predefined ranking of features: In each stage, it then adopts the corresponding feature and only
optimizes over the set of possible scores, instead of optimizing over all features/score pairs.
4. Evaluation
In the following, we compare PSLs constructed solely in a data-driven fashion to PSLs in which
the evaluated features are ordered according to human choices.
4.1. COVID-19 Dataset
We employed a non-public medical dataset, based on the work of Hüfner et al. [26]. A minor
deviation from the original dataset in our study pertains to the exclusion of a single observation
that contained a missing value. Consequently, our dataset has a total of 696 patient observations.
According to the medical tests conducted in the original study, 633 patients (90.95%), tested
negative for COVID-19. This dataset is comprised of 11 binary features, which, apart from
information regarding patient contact with an infected individual, include all patient symptoms.
Figure 1 shows all features, their respective distributions across the entire dataset, and the
distributions for both positive and negative cases. While our dataset does not include additional
demographic information, Hüfner et al. [26] state in their study that 51.1% of the patients were
female and the average age was 55.2 years.
Figure 2 shows the correlation between all features. Quite remarkably, the feature “Contact
with an infected person” is negatively correlated to the target variable. Intuitively, contact
with an infected person and the associated risk of exposure to the virus should have a positive
correlation with an infection. One possible explanation for this peculiarity might be, that
people who know that they had contact with an infected person may have higher awareness
Class
0.6 Overall
Positive Class
Prevalence
Negative Class
0.4
0.2
0.0
Con Cou Dige Fatig Feve Hea Lim Loss Resp Sniffl Sore
tact gh s tive ue r da che b pa of s irator es thro
with prob in mell y sy at
infe lems mpt
cted om
pers
on
Figure 1: Feature prevalence overall and split between positive and negative class.
Cough -0.1
Digestive problems -0.1 0.0
0.2
Fatigue -0.1 0.1 0.2
Fever -0.4 0.1 0.1 0.1
Headache 0.1 -0.0 0.1 0.3 0.0 0.0
Limb pain -0.0 0.1 0.1 0.2 0.2 0.3
Loss of smell -0.0 0.1 0.2 0.2 0.0 0.1 0.2
Respiratory symptom -0.4 -0.0 -0.1 0.0 -0.0 -0.0 -0.0 -0.0 −0.2
Sniffles 0.2 0.0 0.1 0.1 -0.1 0.3 0.1 0.1 -0.1
Sore throat 0.1 0.1 0.1 0.1 -0.1 0.3 0.1 0.1 -0.0 0.2
SARS-CoV-2-PCR positiv -0.1 0.2 0.0 0.3 0.2 0.1 0.2 0.1 0.1 -0.0 0.0 −0.4
Con Cou Dige Fati Feve Hea Lim L R S S
tact gh stive gue r dach b pa oss of espirat niffles ore thr
with prob e in sme ory oat
infe lems l l s y mpt
cted om
pers
on
Figure 2: Heatmap showing features Pearson correlation. The last row shows the correlation with the
target variable.
and hence be tempted to ask for a medical examination more quickly, even when showing no
clear symptoms. This trend is further observable in the first column of the heatmap, where
the correlations with symptoms such as respiratory issues and fever also exhibit a negative
association.
4.2. Experimental Setup
In our experimental evaluation, we use PSLs constructed in five different manners. First, we
consider PSLs derived from training data using the algorithm described in Section 3. These are
called PSL.
Second, we compare them against PSLs built from expert input, specifically the original Covid
Score system proposed by Hüfner et al. [26] (Expert-PSL). The Covid Score was compiled as a
consensus of medical experts. It was evaluated on the proposed dataset, however, it has not
been used in the process of deriving the score. Note that Expert-PSL is a probabilistic scoring
list and thus conceptually different from the original scoring system, which always evaluates
the entire feature set and uses a constant threshold of 5 as a decision rule.
Two further approaches are derived based on a recent incentivized behavioral experiment
conducted by Kornowicz and Thommes [27]. In this study, 234 subjects, recruited from the
Prolific.co1 platform, were requested to rank features based on their perceived importance for
the classification task. Despite these subjects lacking specific medical field expertise, it remains
plausible that the aggregate of their rankings might approximate the quality of expert opinions,
as suggested by research in the field of expert elicitation [28, 29, 30]. We primarily utilized the
rankings generated individually by subjects (Subject-PSL), along with a method of consensus
ranking referred to as Behavioral Aggregation (SubjectBA-PSL). For this method, 90 subjects
were grouped into sets of three to agree upon a collective ranking. As there are no specified
scores attached to the latter, we chose the scores associated with the features in the same greedy,
data-driven manner as the first approach to allow for a fair comparison.
1
https://www.prolific.co/
Table 2
All considered PSLs in the experimental evaluation
Approach Feature sequence chosen algorithmically Scores chosen algorithmically
PSL 3 3
Expert-PSL 7 7
Subject-PSL 7 3
SubjectBA-PSL 7 3
Random-PSL 7 3
Lastly, as a baseline, we consider PSLs constructed from random feature permutations,
for which the scores have been chosen in the same manner (Random-PSL). We chose 𝒮 =
{±1, ±2, ±3} as the set of possible scores for all methods except the expert method. The expert
method’s scores are taken from the scoring system by Hüfner et al. [26] and hence constrained
to 𝒮 = {+1, +2, +3}. An overview of the considered constructions is depicted in Table 2.
We evaluated the individual PSLs in terms of a Monte Carlo cross-validation (MCCV) with
10 repetitions. In each repetition, we use a fraction of two-thirds of the available data as
training data and one-third as test data. We report the expected entropy as a neutral measure
of informativeness at each stage of the decision model in order to compare the approaches.
Additionally, we evaluate the decision models in terms of expected loss minimization. In the
domain of medical decision-making, it is common that a false negative prediction, i.e., not
isolating and treating a COVID-19-infected patient, has far more severe consequences than a
false positive. To capture this, we employ an asymmetric loss function that assigns a loss of 1
to false positives and a loss of 𝑀 ≫ 1 to false negatives. Given the PSLs probabilistic prediction
𝑝 ̂ for the positive class, the risk-minimizing decision is
1 if 1 − 𝑝̂ < 𝑀 ⋅ 𝑝̂
𝑦̂ = { ,
0 otherwise
and the (estimated) expected loss itself by 𝔼(𝑦)̂ = min{1 − 𝑝,̂ 𝑀 ⋅ 𝑝}.
̂ For the experiments, we
chose 𝑀 ≔ 10, i.e., penalizing false negatives ten times as much as false positives.
4.3. Results
In the following, we compare the five different PSL constructions against each other. Figure
3 shows the mean expected entropy and expected loss of the PSLs for each stage, i.e., after
evaluating the stated number of features.
We observe that PSL achieves the best mean expected entropy throughout all stages. The
Subject-PSL and SubjectBA-PSL constructions perform very similar. Up until stage 3, they
exhibit a higher mean expected entropy than the Random-PSL baseline before consistently
outperforming it as off stage 5. The Expert-PSL construction also performs worse than the
random baseline within the first stages, even deteriorating when evaluating the first two features,
both in terms of expected entropy as well as expected loss. This is due to the fact that the first
two features selected by Expert-PSL are “Contact w/ inf. person” and “Respiratory symptom”.
0.45
Expected Entropy 1.0
0.40
Expected Loss
0.8
0.35
0.6
0.30
0.4
0 2 4 6 8 10 0 2 4 6 8 10
Stage Stage
PSL Expert-PSL Subject-PSL SubjectBA-PSL Random-PSL
Figure 3: Mean expected entropy and expected loss of all considered PSL variants trained on the full
training data. Error bands indicate the 95% confidence interval.
As already discussed in Section 4.1, the “Contact w/ inf. person” is negatively correlated with the
target “SARS-CoV-2 positive” and the respiratory symptom is only weakly positively correlated
to it. These two features both receive a score of +3 in the Expert-PSL construction, yielding
poor performances early on and even deteriorating over the performance at stage 0 in which
no feature is considered. The fact that these two features, which seem quite indicative for the
human eye, do not have a strong positive influence on the outcome remains undiscovered for
the experts. Here, the data-driven approach PSL takes advantage of having access to training
data, placing it on average at a rank of 9.
11
Mean Feature Rank
9
7
5
3
1
3
Mean Feature Score
2
1
0
−1
−2
−3
son h s e r e ll s t
f. pe
r Coug problem Fatigu Feve eadach imb pains of sme symptom Sniffle re throa
/ in t ive H L Los iratory So
s
act w Dig e
Resp
Cont
PSL Expert-PSL Subject-PSL SubjectBA-PSL Random-PSL
Figure 4: Average rank and average score of each feature across the methods.
Figure 4 shows an overview of the average ranks and scores of all features across the con-
sidered methods. For many features, the average ranks of the different approaches are quite
similar, with the exception of the “Fatigue” and the “Contact w/ inf. person”. Since the scores
are optimized to the data in all approaches except for the Expert-PSL, the scores are really
similar. This holds true regardless of the average rank of the feature. Note that the expert scores
are selected according to Hüfner et al. [26], constraining them to only positive scores.
Reducing available training data As discussed in the previous section, the data-driven
approach PSL manages to unveil specifics from the data that are not taken into account by
human actors. To make this feasible, it makes use of training data whose availability is a
necessary condition for applying such methods. To investigate how much the data-driven
approaches are dependent on the availability of data, we restricted them to 20% of the original
training data by drawing subsamples from the original data without replacement and repeated
the experiments 10 times. Figure 5 shows the expected entropy of the different PSLs when
training them on these reduced training datasets.
0.45
1.0
Expected Entropy
0.40
Expected Loss
0.8
0.35
0.6
0.30
0.4
0 2 4 6 8 10 0 2 4 6 8 10
Stage Stage
PSL Expert-PSL Subject-PSL SubjectBA-PSL Random-PSL
Figure 5: Mean expected entropy and expected loss of all considered PSL variants trained on a reduced
set of 20% of the original training data. Error bands indicate the 95% confidence interval. Scales have
been chosen in accordance with Figure 3
We observe that the PSL is outperformed from stage 7 on by the Expert-PSL and also by the
Subject-PSL and SubjectBA-PSL as of stage 9 in terms of expected entropy. When it comes
to the expected loss, PSL is already beaten by Expert-PSL at stage 3 and the Subject-PSL
and SubjectBA-PSL methods at stage 7. In the end, even the Random-PSL baseline exhibits a
slightly lower mean expected error than the PSL. As expected, data-driven approaches become
less reliable once access to data is restricted. In such scenarios, human expertise and common
sense achieve better results than automated methods.
5. Conclusion
This paper has explored the comparative effectiveness of humans and algorithms in feature
ranking for decision support. A case study in the medical domain was conducted, in which
we compared feature rankings based on human judgment to rankings automatically derived
from data. It was observed that the data-driven approach can identify patterns and specifics
that remained hidden from human actors, leading to better performances in our experimental
evaluation. On the other hand, feature rankings solely derived in an algorithmic manner bear
the risk of being overfitted to the available training data, resulting in poor generalization per-
formance. This becomes especially important when training datasets are small or significantly
biased. In this case, human knowledge and common sense may be a good countermeasure to
compensate for such effects.
An interactive feature ranking procedure that combines the strengths of human and data-
driven approaches constitutes an interesting direction for future work. Harnessing the benefits
of human expertise and computational analytics in a co-constructive approach potentially
leads to more accurate decision models while mitigating the risk of overfitting. Additionally,
including humans in the learning procedure may also increase the practitioner’s acceptance of
the obtained decision model, as purely algorithmically constructed models are often faced with
distrust [31].
As machine learning-based decision support systems continue to gain traction, our findings
offer valuable insights to researchers in this emerging field. Future research efforts could
potentially build upon and generalize our findings by employing different datasets and extending
the scope to various domains. One of the key strengths of our dataset is the high volume of
human rankings; however, these subjects notably lack significant domain experience, with
the exception of the utilization of the Covid Score system of Hüfner et al. [26]. While the
recruitment of a larger number of domain professionals presents a challenge, pursuing this
could undoubtedly yield more insightful findings in future research.
Acknowledgments
We gratefully acknowledge funding by the German Research Foundation (Deutsche Forschungs-
gemeinschaft, DFG): TRR 318/1 2021 – 438445824.
References
[1] D. Pessach, G. Singer, D. Avrahamia, H. C. Ben-Gal, E. Shmueli, I. Ben-Gala, Employees
recruitment: A prescriptive analytics approach via machine learning and mathematical
programming, Decision Support Systems 134 (2020).
[2] Y. Zhao, D. Zeng, A. Rush, M. Kosorok, Estimating individualized treatment rules using
outcome weighted learning, Journal of the American Statistical Association 107 (2012)
1106–1118. doi:10.1080/01621459.2012.695674 .
[3] M. Ashoori, J. D. Weisz, In ai we trust? factors that influence trustworthiness of ai-infused
decision-making processes, arXiv:1912.02675 [cs] (2019). URL: http://arxiv.org/abs/1912.
02675, arXiv: 1912.02675.
[4] T.-H. Cheng, C.-P. Wei, V. Tseng, Feature selection for medical data mining: Comparisons of
expert judgment and automatic approaches, in: 19th IEEE Symposium on Computer-Based
Medical Systems (CBMS’06), 2006, p. 165–170. doi:10.1109/CBMS.2006.87 .
[5] A. Filippova, C. Gilroy, R. Kashyap, A. Kirchner, A. C. Morgan, K. Polimis, A. Usmani,
T. Wang, Humans in the loop: Incorporating expert and crowd-sourced knowledge
for predictions using survey data, Socius 5 (2019) 2378023118820157. doi:10.1177/
2378023118820157 .
[6] J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, H. Liu, Feature selection: A
data perspective, ACM Computing Surveys 50 (2017) 94:1–94:45. doi:10.1145/3136625 .
[7] A. G. Rapsang, D. C. Shyam, Scoring systems in the intensive care unit: A compendium,
Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian
Society of Critical Care Medicine 18 (2014) 220–228. doi:10.4103/0972- 5229.130573 .
[8] B. Ustun, C. Rudin, Supersparse linear integer models for optimized medical scoring
systems, Machine Learning 102 (2016) 349–391. doi:10.1007/s10994- 015- 5528- 6 .
[9] G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning, volume
112, Springer, 2013.
[10] S. Studer, T. B. Bui, C. Drescher, A. Hanuschkin, L. Winkler, S. Peters, K.-R. Müller, Towards
crisp-ml(q): A machine learning process model with quality assurance methodology,
Machine Learning and Knowledge Extraction 3 (2021) 392–413. doi:10.3390/make3020020 .
[11] D. C. Corrales, E. Lasso, A. Ledezma, J. C. Corrales, Feature selection for classification
tasks: Expert knowledge or traditional methods?, Journal of Intelligent & Fuzzy Systems
34 (2018) 2825–2835. doi:10.3233/JIFS- 169470 .
[12] J. Nahar, T. Imam, K. S. Tickle, Y.-P. P. Chen, Computational intelligence for heart disease
diagnosis: A medical knowledge driven approach, Expert Systems with Applications 40
(2013) 96–104. doi:10.1016/j.eswa.2012.07.032 .
[13] J. Wang, J. Oh, H. Wang, J. Wiens, Learning credible models, in: Proceedings of the 24th
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD
’18, Association for Computing Machinery, New York, NY, USA, 2018, p. 2417–2426. URL:
https://doi.org/10.1145/3219819.3220070. doi:10.1145/3219819.3220070 .
[14] S. Moro, P. Cortez, P. Rita, A divide-and-conquer strategy using feature relevance and
expert knowledge for enhancing a data mining approach to bank telemarketing, Expert
Systems 35 (2018) e12253. doi:10.1111/exsy.12253 .
[15] A. Holzinger, Interactive machine learning for health informatics: when do we
need the human-in-the-loop?, Brain Informatics 3 (2016) 119–131. doi:10.1007/
s40708- 016- 0042- 6 .
[16] A. H. C. Correia, F. Lecue, Human-in-the-loop feature selection, Proceedings of the AAAI
Conference on Artificial Intelligence 33 (2019) 2438–2445. doi:10.1609/aaai.v33i01.
33012438 .
[17] F. Bianchi, L. Piroddi, A. Bemporad, G. Halasz, M. Villani, D. Piga, Active preference-based
optimization for human-in-the-loop feature selection, European Journal of Control 66
(2022) 100647. doi:10.1016/j.ejcon.2022.100647 .
[18] V. Bolón-Canedo, A. Alonso-Betanzos, Ensembles for feature selection: A review and
future trends, Information Fusion 52 (2019) 1–12. doi:10.1016/j.inffus.2018.11.008 .
[19] D. J. Dittman, T. M. Khoshgoftaar, R. Wald, A. Napolitano, Classification performance of
rank aggregation techniques for ensemble gene selection, in: The twenty-sixth interna-
tional FLAIRS conference, 2013.
[20] R. Wald, T. M. Khoshgoftaar, D. Dittman, W. Awada, A. Napolitano, An extensive com-
parison of feature ranking aggregation techniques in bioinformatics, in: 2012 IEEE 13th
International Conference on Information Reuse & Integration (IRI), 2012, p. 377–384.
doi:10.1109/IRI.2012.6303034 .
[21] I. Guyon, A. Elisseeff, An introduction to variable and feature selection, Journal of machine
learning research 3 (2003) 1157–1182.
[22] S. McKay, When 4≈ 10,000: The power of social science knowledge in predictive perfor-
mance, Socius 5 (2019) 2378023118811774.
[23] A. Six, B. Backus, J. Kelder, Chest pain in the emergency room: value of the heart score,
Netherlands Heart Journal 16 (2008) 191–196.
[24] C. Wang, B. Han, B. Patel, C. Rudin, In pursuit of interpretable, fair and accurate machine
learning for criminal recidivism prediction, Journal of Quantitative Criminology (2022)
1–63.
[25] J. Hanselle, F. Fürnkranz, E. Hüllermeier, Probabilistic scoring lists for interpretable
machine learning, in: Proc. DS, 23rd Int. Conference on Discovery Science, Springer, Porto,
Portugal, 2023.
[26] A. Hüfner, D. Kiefl, M. Baacke, R. Zöllner, E. Loza Mencía, O. Schellein, N. Avan,
S. Pemmerl, Risikostratifizierung durch implementierung und evaluation eines covid-19-
scores, Medizinische Klinik - Intensivmedizin und Notfallmedizin 115 (2020) 132–138.
doi:10.1007/s00063- 020- 00754- 4 .
[27] J. Kornowicz, K. Thommes, Aggregating human domain knowledge for feature ranking, in:
H. Degen, S. Ntoa (Eds.), Artificial Intelligence in HCI, Lecture Notes in Computer Science,
Springer Nature Switzerland, Cham, 2023, p. 98–114. doi:10.1007/978- 3- 031- 35891- 3_
7.
[28] D. Önkal, J. F. Yates, C. Simga-Mugan, Ş. Öztin, Professional vs. amateur judgment
accuracy: The case of foreign exchange rates, Organizational Behavior and Human
Decision Processes 91 (2003) 169–185. doi:10.1016/S0749- 5978(03)00058- X .
[29] M. Nofer, Are Crowds on the Internet Wiser than Experts? – The Case of a Stock Prediction
Community, Springer Fachmedien, Wiesbaden, 2015, p. 27–61. URL: https://doi.org/10.
1007/978-3-658-09508-6_3. doi:10.1007/978- 3- 658- 09508- 6_3 .
[30] E. Vul, H. Pashler, Measuring the crowd within: Probabilistic representations within
individuals, Psychological Science 19 (2008) 645–647. doi:10.1111/j.1467- 9280.2008.
02136.x .
[31] H. Mahmud, A. K. M. N. Islam, S. I. Ahmed, K. Smolander, What influences algorithmic
decision-making? a systematic literature review on algorithm aversion, Technological Fore-
casting and Social Change 175 (2022) 121390. doi:10.1016/j.techfore.2021.121390 .