Towards a visual framework for the incorporation of knowledge in the phases of machine learningCJ Swanepoel0KM Malan0Department of Decision Sciences, University ofSouth Africa
Incorporating domain knowledge into machine learning algorithms to some extent is almost unavoidable. Doing it well and explicitly can avoid unnecessary bias, improve efficiency and accuracy, and increase transparency. To increase an awareness of the relative contributions of domain knowledge and machine learning expertise, as well as an indication of the direction of information flow, a tentative qualitative visualisation framework is suggested, and two examples are given. It is hoped that such a mechanism will encourage reflection on the (sometimes implicit and innate) inclusion of domain knowledge in machine learning systems.
Machine learning as a component of artificial intelligence, and especially deep
learning, has experienced phenomenal growth over the last couple of years. (For
example, in 1998 there were two papers with primary subcategory ‘Machine
learning’ on arXiv, and in 2017 there were 2 332 papers [
19
].) This growth is
the result of the advances in computing power, especially through the use of
graphics processing units and more recently tensor processing units, the ubiquity
of available data, the connectivity afforded by the internet, and major advances
in machine learning algorithms by Bengio, Hinton and LeCun, amongst others
[
14
]. In well defined domains where data is plentiful or cheap to generate and
where the context is stable, machine learning can be an extremely useful tool.
However, many machine learning techniques (and especially deep learning) are
fragile, greedy, shallow and not transparent [
8, 12, 27
]. The techniques are fragile
because transfer to a slightly different domain or context usually breaks them;
greedy because they require huge amounts of (labelled) data; shallow because
they depend on superficial features and do not possess an underlying model
based on the physical reality; and not transparent because the internal structure
is often too complex to analyse and connect to the features that determine
the output. This necessitates careful attention to subtle aspects of the machine
learning development process.
This paper focusses on the inclusion of domain knowledge in machine learning,
because this is one factor that can affect the fragility, greediness, shallowness
and lack of transparency of machine learning. The aim is to provide a
framework to assist practitioners to make explicit the different ways in which domain
knowledge and machine learning expertise are incorporated in the stages of
machine learning. Being aware of subtle inclusions of domain knowledge and innate
knowledge endowed to the machine learning system might assist in identifying
opportunities to improve its performance, accuracy or even transparency, and
can contribute to more accurate reporting of machine learning system designs.
2
The inclusion of domain knowledge
The recent successes of machine learning that depend on data only, and use
‘no domain specific’ data (e.g. AlphaZero) create the impression that domain
specific knowledge is not really necessary in machine learning, and might even
reduce the ‘generality’ of the resulting system. The practical difficulties in finding
useful representations of expert knowledge, and the fact that domain knowledge
is often not complete or perfect, reinforces this view [
27
].
Two seemingly diametrically opposed views are expressed in recent literature
[
15, 13
]: on the one hand, the view that all problems can be solved by scaling the
model up and rely on the data only (AlphaZero, for example), and on the other
hand, the belief that using a combination of data and domain knowledge will
eventually prove to be the best approach (an idea already propounded by Alan
Turing in his 1950 paper [
23
]). The ‘data only’ camp demonstrated spectacular
results, particularly in the domain of game play and text generation, although
the fear exists that it will not generalise easily (mainly due to data constraints
in most domains) and that the performance will hit a ceiling.
Even when domain knowledge is not explicitly injected (the ‘data only’
approach), implicit domain knowledge almost always features in machine learning
[
9
]. This implicit or innate domain knowledge can include the choice and
structure of the algorithm, representational formats, and innate knowledge or
experience [
13
]. For example, the choice of data encoding method depends partly on
an understanding of the problem domain, and can greatly influence the
effectiveness of the algorithm (see, e.g. [
7, 18
]). Feature selection is often task dependent
and sometimes even based on intuition [
5, 26
]. The type of algorithm used also
depends heavily on the nature of the problem domain. Marcus [
13
] quotes
Pedro Domingos: “[Machine learning] paradigms differ in what assumptions they
encode, and what form of additional knowledge they make it easy to encode.”
The structure of a deep learning neural network is influenced by the nature of
the problem. In the description of the neural network architecture for AlphaGo
Zero, where emphasis was placed on using as little as possible explicit domain
knowledge, it is stated that “History features Xt, Yt are necessary, because Go is
not fully observable solely from the current stones. . . ” [
21
]. Further examples of
the extensive embedding of human domain specific knowledge in the construction
of AlphaGo Zero and AlphaZero are given in [
13
].
In a genetic algorithm the choice of cross-over and mutation mechanisms will to
some extent depend on implicit domain knowledge [
10
]. Risk or loss functions
include implicit domain knowledge, but can also encode selected prior knowledge
[
15, 22
]. Constraining the output of a machine learning system based on heuristics
or rules from the problem domain is common practice. (For example, all Go
playing algorithms before AlphaGo Zero routinely removed stupid (but legal)
moves [
21
].)
In most (non-game) domains there are additional considerations for including
domain knowledge, such as the fact that data can be difficult to get, or expensive,
or might include bias, or be unbalanced (for example, the lack of edge cases). It is
often necessary to explicitly include additional information to get to a feasible,
unbiased or useful solution [
4, 6
]. The explicit inclusion of domain knowledge
can potentially also improve the transparency of the model [
28
]. Conversely,
including less explicit domain knowledge might lead to more general algorithms.
There are some obstacles to the inclusion of domain knowledge, though. These
include the difficulty of finding workable encodings and injection mechanisms,
the fact that most domain experts are not data science experts and vice versa
[
27
]. The restrictions introduced by injecting domain knowledge can potentially
also prevent the discovery of valid but unexpected solutions [
3
].
The explicit integration of knowledge into machine learning is called ‘informed
machine learning’ by von Rueden et al. [
25
]. They developed a taxonomy for
the explicit integration of knowledge. Their proposed taxonomy contains three
components: the type of knowledge, the method used to integrate the knowledge
into the machine learning system, and lastly where in the machine learning
pipeline the integration happens. It is a useful tool to classify papers in the
assisted or informed machine learning domain.
However, the implicit inclusion of domain knowledge in the form of innate
knowledge and convention or experience is often not recognised and neglected in the
reporting on and meta-analysis of machine learning algorithms. Many of the
choices made throughout all phases of the machine learning process are based
on some understanding of aspects of the problem or task, augmented by
expertise and experience in the machine learning domain. This paper attempts to
provide a tentative high level framework to visually characterise the inclusion of
any domain knowledge into machine learning.
3
Proposed framework
In Figure 1 the generic phases of the machine learning pipeline are given as
coloured blocks labelled with a capital letter. A very brief description of the
phases is given in Table 1, and a more comprehensive account is given in
Section 4.
Although the phases are listed more or less in the order in which they occur
in the machine learning pipeline, it is an idealised representation that does not
necessarily reflect the actual workflow of a specific system – in practice the order
in which the phases are executed can be convoluted, and might include several
iterative loops. The colours group the phases into six clusters: problem
formulation (green), data preparation (yellow), machine learning activities (blue),
output constraints (red), interpretation and explanation (brown), and external
comparison (green).
A subjective qualitative estimate of the magnitude of the contribution from
respectively domain knowledge and machine learning is represented by the relative
thicknesses of the arrows in the figure. In cases where the emphasis is on the
exploitation of data only, many of the arrows from domain knowledge will be
thin or have zero thickness, for example. The direction of the arrows indicates
the direction of information flow. Such a representation will be unique to every
particular instance of a machine learning system, and can give a quick visual
overview of the nature of the interactions in that instance.
The representation is subjective, and as such cannot provide accurate or
quantitative data. However, this simplified model allows a quick high-level evaluation
of the relative contributions from the two knowledge domains.
A: Problem identification and
formula
tion
B: Data sourcing/labelling
C: Data cleaning/validation/quality
eval
uation
D: Data augmentation
E: Data encoding
F: Machine learning algorithm selection
G: Feature engineering
H: Machine learning algorithm structure
N: Comparative evaluation
A B C D E F G H I J K L M N
Phases of the machine learning pipeline
A: Problem identification and formulation Thorough knowledge of the
domain is necessary for this step. The context will determine what data can
be collected, what the objective(s) is (are), and what information might be
available that is not included in the data.
B: Data sourcing/labelling Although the machine learning process often starts
with available data, in some cases data will have to be sourced or labelled.
Domain knowledge in the form of deep knowledge of the relationships
between different features, the nature of the data, the difficulty and cost of
labelling as well as an understanding of machine learning algorithms and
how the data will be used can contribute to the quality of the data that is
eventually used, and hence influence the outcome or success of the machine
learning process.
C: Data cleaning/validation/quality evaluation Knowledge of the
properties of the domain and the data collection methodology can assist in
identifying outliers or invalid data points, and allow for an evaluation of the
quality of the data. It will also give insight into the coverage of the data
space (whether there are areas where data is too sparse to be useful).
D: Data augmentation Both knowledge of the domain and the machine
learning algorithm is required for the successful augmentation of data. The
imputation of missing data points, for example, can take various forms, and some
of the techniques might be counterproductive in the training of a model.
Another example: the perturbation of images by shifting a few pixels
horizontally or vertically to provide additional training data assumes an
understanding that such a translation will indeed provide novel information to the
machine learning system, while it remains valid as a representation and does
not influence the labelling of the image.
E: Data encoding Mainly machine learning expertise is required. However,
a deep understanding of the domain knowledge environment is assumed.
For example, one-hot encoding can be difficult or impossible to work with
when the feature space is very large. Piech et al. [
17
] address this data
encoding challenge by utilising a random low-dimensional representation of
a one-hot high-dimensional vector. This encoding is motivated by the idea
of compressed sensing introduced by Baraniuk in 2007 [
1
] as an effective
method to capture and represent compressible signals at a rate significantly
below the Nyquist rate. Another beautiful example is given in the paper
by Lusci et al. [
11
] where molecules are represented as ensembles of directed
acyclic graphs as input to a recursive neural network to predict the solubility
of these molecules.
F: Machine learning algorithm selection This deals with the choice of a
suitable machine learning algorithm. A deep understanding of the working of
different machine learning techniques is required, as well as an understanding
of the nature of the problem domain. For example, if the problem has a
temporal component, a recurrent neural network might be a good fit, or
if filtering is required, an auto-encoder should be considered. Also see the
paper by Olson et al. [
16
] where thirteen machine learning algorithms are
compared over 165 publicly available classification problems.
G: Feature engineering Feature engineering includes binning, transformation
of features, scaling, grouping operations and feature selection. Domain
knowledge in the form of an understanding of the relationships between features
and the information content of different features are required.
H: Machine learning algorithm structure determination This is sometimes
described as more of an art than a science. Typically the structure of the
machine learning algorithm is determined empirically through
experimentation. Here experience with similar or related problems, which is a form of
domain knowledge, plays a huge role. For example, Chandrasekaran et al.
[
2
] in a recent paper designed a machine learning predictor for density of
state and charge density of a material or molecule. For the charge density,
the modelling is done with a simple fully connected neural network with one
output neuron. The local density of states spectrum, on the other hand, is
modelled with a recurrent neural network, where the local density of state at
every energy window is represented as a single output neuron (linked via a
recurrent layer to other neighbouring energy windows). Domain knowledge
therefore played a huge part in determining the structure of the machine
learning system.
I: Learning process mechanisms (transfer function / learning mechanism /
mutation operator, etc. selection) This is probably the area where there
is the biggest opportunity of innovation. Novel functions for cross-over, or
innovative transfer functions can hugely influence the way in which the search
space is traversed.
J: Hyperparameter tuning As with stage H, this is an area that is mostly
approached empirically. The performance metric used in the hyperparameter
optimisation process is influenced by domain knowledge. Setting ranges for
grid or random searches to optimise hyperparameters, as well as determining
of which of the hyperparameters should be included in the search cannot be
analytically determined, but knowledge of the hyperparameter behaviour in
related problems can provide a good starting point for an empirical search
strategy.
K: Constraining outputs This is one of the most important mechanisms to
include domain knowledge in the machine learning process. Techniques used
include the augmentation or restriction of the loss or risk function, and
filtering or transforming intermediate outputs or the final output. Stewart
and Ermon [
22
], for example, use laws from physics to constrain the output
space in training a convolutional neural network to track objects without
using any labelled examples.
L: Interpretation / validation A good understanding of the underlying
knowledge domain will allow an evaluation of the feasibility or quality of the
outcomes. In simple classification problems this is not really an issue, but for
many decision support applications this is essential. The output of a machine
learning algorithm might also increase our understanding of the domain,
hence the possibility of a two-way arrow in the proposed framework.
M: Explanation One of the biggest criticisms against many machine learning
techniques is the lack of transparency, or the ability to explain the output
of the machine learning system. Legal and moral requirements dictate that
in certain environments it should be possible to justify the outcomes in the
light of the inputs. Here an understanding of the domain complexities as well
as the machine learning mechanism is required – although this might not be
sufficient in many cases. In the proposed framework the direction of the
arrow towards the domain knowledge bar indicates the flow of information
that might add to our understanding of the domain. In addition, an analysis
of how the machine learning process obtained the outputs could add to
machine learning expertise.
N: Comparative evaluation Comparing the performance of a machine
learning algorithm against previously applied approaches (benchmarking) is often
necessary to evaluate the performance of a new approach. This also
contributes to the knowledge base of machine learning.
5
Case studies/examples
In this section two recent contributions to the machine learning environment are
described briefly, and the proposed visual representations of knowledge are given
for both.
5.1
Combination of domain knowledge and deep learning for
sentiment analysis, by Vo et al.
In the paper ‘Combination of domain knowledge and deep learning for sentiment
analysis’, published in 2017, Vo et al. [
24
] found that existing approaches in the
application of machine learning to sentiment analysis suffer from two major
drawbacks, the first of which is that until then nobody has paid attention to
the different types of sentiment terms. Different domains use different terms to
express positive and negative sentiments, and some words carry a higher emotive
content than others. Secondly, the loss functions used previously did not include a
measure of the magnitude of sentiment misclassification, and did not distinguish
between different types of misclassification.
To address these two issues, they proposed using sentiment scores (learnt by
quadratic programming) to augment training data; and introduced a penalty
matrix to enhance the loss function. The enhancements were applied to a
standard sentiment analysis workflow using a convolutional neural network. To
evaluate the success of their approach, they compared the performance of the new
system with a baseline convolution neural network sentiment analyser, as well as
with a traditional support vector machine based sentiment analyser. In the
comparative analysis the new approach performs better than the previous versions,
showing that the inclusion of the two enhancements are useful in this domain.
This knowledge and the description of the novel enhancements led to the
diagram in Figure 2, where the incorporation of additional domain knowledge in the
‘data augmentation’ (D) and ‘constraining outputs’ (K) phases is emphasised.
Domain Knowledge
A B C D E F G H I J K L M N
Machine Learning Expertise
During 2016 and 2017 DeepMind released three versions of their Go playing
software. The first system, now called AlphaGo Lee, defeated the world champion
Lee Seedol 4–1 in a five game match in March 2016. AlphaGo Lee used two
neural networks – a ‘policy’ and a ‘value’ network, as well as a Monte Carlo tree
search algorithm. It was trained using historic game information, and improved
through self play. A second version, AlphaGo Zero, was introduced in a paper
in Nature on 19 October 2017 [
21
]. The title of the paper: ‘Mastering the game
of Go without human knowledge’ expresses the major claim of this version –
that it used no human or domain knowledge except for the rules of the game. A
third version of the software (AlphaZero) was introduced in a paper published on
arXiv in December 2017 [
20
]. This version added chess and shogi to the repertoire
of games mastered by the system. It was claimed that in this progression the
newer version each time learnt quicker and exceeded the performance of its
predecessor. The contribution of knowledge into the second version, AlphaGo
Zero, is discussed in this section.
In [
13
] Marcus extensively discusses different inclusions (some not mentioned in
the original paper) of domain knowledge into the AlphaGo Zero machine learning
system. He argues that the use of carefully constructed Monte Carlo tree search
machinery, the artful placement of convolutional layers that allow the system
to recognise that many patterns on the board are translation invariant, and the
application of a sampling algorithm for dealing with reflections and rotations
constitute the injection of domain knowledge into the system.
Based mostly on Marcus’s assessment, rough qualitative judgements on the
inclusion of domain knowledge into AlphaGo Zero were made that are reflected in
Figure 3.
Domain Knowledge
A B C D E F G H I J K L M N
Machine Learning Expertise
The lack of explicit contributions from domain knowledge in the phase of
‘constraining outputs’ (K) is indicative of the fact that this popular method of
injecting domain knowledge was not used. There are, however, substantial
contributions in phases F and H, the selection of the machine learning algorithm (a
combination of Monte Carlo search trees and reinforcement learning) and the structure
determination of the algorithm (taking into account symmetries, for example).
The direction of information flow in the three phases ‘interpretation/validation’
(L), ‘explanation’ (M) and ‘comparative evaluation’ (N) should also be noted.
Domain knowledge and machine learning expertise are expanded to varying
degrees with a successful implementation of a machine learning system. In the
case of AlphaGo Zero new (‘alien’) gameplay strategies were discovered
(domain knowledge), and some insight gained into the comparative performance of
different approaches (machine learning expertise).
Conclusion
A visualisation scheme for the inclusion of domain knowledge and machine
learning expertise into machine learning systems is proposed. The hope is that it will
aid in greater awareness of the innate, implicit and explicit use of domain
knowledge in the machine learning workflow.
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