Adaptive Class Association Rule Mining for Human Activity Recognition

Abstract

The analysis of human activity data is an important research area in the context of ubiquitous and social environments. Using sensor data obtained by mobile devices, e. g., utilizing accelerometer sensors contained in mobile phones, behavioral patterns and models can then be obtained. However, the utilized models are often not simple to interpret by humans in order to facilitate assessment, evaluation and validation, e. g., in computational social science or in medical contexts. In this paper, we propose a novel approach for generating interpretable rule sets for classication: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze dierent techniques for obtaining the nal classier. The approach is investigated in the context of human activity recognition. For our evaluation, we apply real-world activity data collected using mobile phone sensors.

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With more and more ubiquitous devices emerging in our daily lives, sensor data capturing human activities is becoming a universal data source for the analysis of human behavioral patterns, and for building according models. However, often such models are either black-box models like neural networks, or are rather complex, e. g., in the case of random forests or large decision trees. Rule-based models can then often provide simpler models with comparable accuracy, estimated using quality measures [ 6, 7 ], in order to facilitate human interpretation.

Copyright c 2015 by the paper’s authors. Copying permitted only for private and academic purposes. In: M. Atzmueller, F. Lemmerich (Eds.): Proceedings of 6th International Workshop on Mining Ubiquitous and Social Environments (MUSE), co-located with the ECML PKDD 2015. Published at http://ceur-ws.org

In this paper, we propose a novel approach for class association rule mining using subgroup discovery. We present an adaptive framework for mining such rules, and demonstrate the eectiveness of the proposed approach using realworld activity data collected using mobile phone sensors. Specically, we focus on activity recognition , as a prominent research eld with respect to the classication of human activities.

Class association rules are special association rules with a xed class attribute in the rule consequent. In order to mine such rules, we apply subgroup discovery [ 4,42 ] an exploratory approach for discovering interesting subgroups dened by a description, e. g., a conjunction of attributevalue pairs (i. e., a typical rule body) with respect to a binary target concept. In the case of class association rules, the respective class can be dened as the target concept (i. e., the rule head). Then, subgroup discovery can be adapted as a rule generator for class association rule mining. As we will discuss below, there are further adaptations for mining the nal rule set, which we integrate into a comprehensive framework for adaptive class association rule mining.

Our contribution can be summarized as follows: 1. We adapt subgroup discovery to class association rule mining, and embed it into an adaptive approach for obtaining a rule set that aims to target a simple rule base with an adequate level of predictive power, i. e., combining simplicity and accuracy. 2. For constructing the rule base, we utilize standard methods of rule selection and evaluation, and demonstrate the integration into our framework. 3. We provide an evaluation using real-world activity data obtained by mobile phone sensors, and demonstrate the eectiveness of our approach by a comparison with typical descriptive models, i. e., using Ripper as a rule-based baseline, and C4.5 as a decision tree classier.

The rest of the paper is structured as follows: Section 2 discusses related work. Then, Section 3 introduces the necessary background. After that, Section 4 introduces the adaptive framework for class association rule mining. In Section 5 we describe the applied dataset. Next, Section 6 presents the results of our experiments and discusses them in detail. Finally, Section 7 concludes with a summary and provides interesting options for future work. 2

Related Work

Below, we discuss related work concerning general approaches for the classication of sensor data, subgroup discovery and associative classication. 2.1

Classication and Sensor Data Classication of activities based on sensor data is a prominent research area. Several authors investigated the topic using wearable sensors, e. g., as also integrated into mobile phones. These sensors can be attached to parts of the body like arms, legs or the hip. The rst works in this regard were already done at the end of the 1990s [ 30 ]. In the research of Foerster et. al. [ 23 ] 24 participants wore sensors on sternum, wrist, thigh and the lower leg. Nine activities were then replicated. Also, Bao and Intille [ 16 ] asked 20 subjects to perform some everyday activities while wearing ve biaxial accelerometers on dierent parts of the body.

Fabian et al. [ 21 ] developed a real-time mobile system to recognize six different activities in both standing and sitting positions. Therefore three motion band devices were attached to the wrist, hip and the dominant ankle of the participants. These devices contained an accelerometer, a magnetometer and a gyroscope. While the training was done oine on a Desktop PC, the following recognition process was done in real time with a smartphone collecting the sensor data from the attached motion bands.

In this paper, we consider the eld of wearable sensors, specically on those embedded in mobile phones, focusing on the accelerometer: Kwapisz et al. [ 29 ], for example, collected and labeled data from 29 users and tried to classify six basic activities (like standing or walking). Reddy et al. [ 39 ] considered the problem of usage of mobile phones to determine transportation mode (such as walking, biking, or in motorized transport) and used additionally GSM receiver of the device. Berthold et al. [ 17 ] presented ActiServ an architecture which creates an evolving activity classication system using feedback from the user community. Yang [ 43 ] proposed a physical activity diary based on automatic sensor data classication to use in mobile healthcare and further applications (currently such applications emerge, e. g., Apple ResearchKit 4).

In contrast to most of the presented works, we concentrate on some special activities, some of which assume active interaction with mobile phones. We also dene a group of disrupt activities activities which are similar to a usual activity to examine if the presented classier may recognize small dierences in activities. Furthermore, we consider up to 8 sensors for improving activity recognition. In contrast, related work discussed above only uses accelerometer or in a few cases a limited number of two or three sensors. 2.2

Subgroup Discovery Subgroup discovery [ 2, 4, 15, 27, 42 ] has been established as a general and broadly applicable technique for descriptive and exploratory data mining: It aims at identifying descriptions of subsets of a dataset that show an interesting behavior with respect to certain interestingness criteria, formalized by a quality function, e. g., [ 4, 25, 27 ].

Overall, subgroup discovery and analytics are important tools for descriptive data mining: They can be applied, for example, for obtaining an overview on the relations in the data, for automatic hypotheses generation, and for data exploration. Prominent application examples include knowledge discovery in medical, technical, and social domains, e.g., [ 3, 10, 14, 15, 24, 31, 37 ]. Subgroup discovery is 4 http://researchkit.org especially suited for identifying local patterns in the data, that is, nuggets that hold for specic subsets: It can uncover hidden relations captured in small subgroups, for which variables are only signicantly correlated in these subgroups. Typically, the discovered patterns are especially easy to interpret by the users and domain experts, cf. [ 11, 24, 25 ].

Standard subgroup discovery approaches commonly focus on a single target concept as the property of interest [ 25, 27, 31 ], while the quality function framework also enables multi-target concepts , e. g., [ 12,28 ]. Furthermore, more complex target properties [ 20, 32 ] can be formalized as exceptional models , cf. [ 32 ]. In the case of a binary target variable, the share in a subgroup can be compared to the share in the dataset in order to detect deviations in (large) subgroups. This is also the approach considered in this paper, where we focus on a specic class (a set of classes, respectively) as the target concept(s). In addition to basic subgroup discovery which aims at providing the obtained subgroups in exploratory and descriptive fashion, we embed subgroup discovery as the basis of our rule generation approach. We apply an adaptive method that aims to generate rules with increasing complexity (and accuracy) based on a performance estimate of the current subgroup set. In addition, we apply a rule selection strategy in order to obtain the nal set of class association rules for classication. 2.3

Associative Classication Associative classication approaches integrate association rule mining and classication strategies. Thabtah [ 41 ] provides a survey on the eld. This includes the rst approach by Liu et al. [ 35 ] for class association rule mining, which includes association rule mining and subsequent rule selection in the CBA algorithm. It applies a covering strategy, selecting rules one by one, minimizing the total error. Alternative approaches include the CMAR algorithm by Li et al. [ 34 ] which also applies covering, but allows for multiple rules to cover an instance. The CPAR algorithm by Yin and Han [ 44 ] integrates rule mining and selection, and achieves comparable accuracy compared to CBA and CMAR. In addition to the rule mining and selection techniques, there are several strategies for the nal decision of how to combine the rules for the classication (voting of the matching rules), e. g., [ 40 ].

Compared to the approaches discussed above, our proposed approach applies subgroup discovery for class association rule mining, which allows for suitable selection of a (complex) quality function for mining the rules, in constrast to the (simple) condence/support-based approaches applied by association rule mining approaches. Then, for example, signicance criteria can be simply embedded. Furthermore, the presented approach applies an adaptive strategy for balancing rule complexity (size) with predictive accuracy by applying a ruleset assessment function, in addition to the rule selection function. However, our framework is general in that respect, that we do not enforce a specic strategy. Instead, this decision can be congured by the specic implementation of the framework. In our implementation throughout this paper, for example, we follow the rule selection strategy of CBA; the ruleset assessment is done by a median-based ranking of the according condences of the rules, i. e., estimated by the respective shares of the class contained in the subgroups covered by the respective rules. We will describe these concepts below in more detail. 3

Background

Below, we rst introduce some basic notation. After that, we summarize basics on subgroup discovery, before we sketch how to mine class association rules using subgroup discovery. 3.1

Basic Notation Formally, a database DB = (I; A) is given by a set of individuals I and a set of attributes A. A selector or basic pattern sel ai=vj is a Boolean function I ! f0; 1g that is true if the value of attribute ai 2 A is equal to vj for the respective individual. The set of all basic patterns is denoted by S.

For a numeric attribute anum selectors sel anum2[minj;maxj] can be dened analogously for each interval [minj ; maxj ] in the domain of anum. The Boolean function is then set to true if the value of the respective attribute anum is within the respective interval. 3.2

Patterns and Subgroups Basic elements used in subgroup discovery are patterns and subgroups. Intuitively, a pattern describes a subgroup, i. e., the subgroup consists of instances that are covered by the respective pattern. It is easy to see, that a pattern describes a xed set of instances (subgroup), while a subgroup can also be described by dierent patterns, if there are dierent options for covering the subgroup’ instances. In the following, we dene these concepts more formally.

Denition 1. A subgroup description or (complex) pattern sd is given by a set of basic patterns sd = fsel 1; : : : ; sellg ; where sel i 2 S, which is interpreted as a conjunction, i.e., sd (I) = sel 1 ^ : : : ^ sel l, with length(sd ) = l.

Without loss of generality, we focus on a conjunctive pattern language using nominal attributevalue pairs as dened above in this paper; internal disjunctions can also be generated by appropriate attributevalue construction methods, if necessary.

Denition 2.

A subgroup (extension)

sg sd := ext (sd ) := fi 2 Ijsd (i) = trueg is the set of all individuals which are covered by the pattern sd .

As search space for subgroup discovery the set of all possible patterns 2S is used, that is, all combinations of the basic patterns contained in S. Then, appropriate ecient algorithms, e. g., [ 8, 13, 33 ] can be applied. 3.3

Interestingness of a Pattern

(1) (2) The interestingness of a pattern is determined by a quality function, which is selected according to the analysis task.

Denition 3. A quality function q : 2S ! R maps every pattern in the search space to a real number that reects the interestingness of a pattern (or the extension of the pattern, respectively).

While a large number of quality functions has been proposed in literature, many quality functions for a single target concept, e. g., in the binary or numerical case, trade-o the size n = jext(sd )j of a subgroup and the deviation tsd t0, where tsd is the average value of a given target concept in the subgroup identied by the pattern sd and t0 the average value of the target concept in the general population. In the binary case, the averages relate to the share of the target concept. Thus, typical quality functions are of the form qa(sd ) = na (tsd t0); a 2 [0; 1] : For binary target concepts, this includes, for example, the weighted relative accuracy for the size parameter a = 1 or a simplied binomial function, for a = 0:5. An extension to a target concept dened by a set of variables can be dened similarly, by extending common statistical tests.

While a quality function provides a ranking of the discovered subgroup patterns, often also a statistical assessment of the patterns is useful in data exploration. Quality functions that directly apply a statistical test, for example, the Chi-Square quality function, e. g., [ 4 ] provide a p-Value for simple interpretation. However, the Chi-Square quality function estimates deviations in two directions. An alternative, which can also be directly mapped to a p-Value is given by the adjusted residual quality function qr, since the values of qr follow a large standard normal distribution, cf. [ 1 ]: 1 qr = n(tsd t0)

pnt0(1 t0)(1 Nn )

The result of top- k subgroup discovery is the set of the k patterns sd 1; : : : ; sd k ; where sd i 2 2S with the highest interestingness according to the applied quality function. A subgroup discovery task can now be specied by a 5-tuple: (DB; c; S; q; k) : We focus on the case of a binary target concept c : I ! < specifying the property of interest: In the context of class assocation rule mining, it maps each instance in the dataset to a target value c corresponding to the respective class of the instance. The search space 2S is dened by set of basic patterns S.

Furthermore, we consider additional constraints with respect to the complexity of the patterns. We can restrict the length l of these descriptions to a certain maximal value, e. g., with length l = 1 we only consider subgroup descriptions containing one selector, with length l = 2 we consider a conjunction of two selectors etc. Then, the complexity of the discovered patterns can also be adaptively adjusted as described in Section 4.

Subgroup Discovery for Mining Class Association Rules For mining class association rules, we apply subgroup discovery, such that for every class c 2 S, we create an according target concept c. Then, we discover a set of the top-k patterns CARc = fsd c1; sd c2; : : : ; sd ckg for each target concept. It is easy to see, that a subgroup pattern directly corresponds to a class association rule - the head of the rule is given by the target concept, while the body of the rule is given by the specic subgroup description. Then, these rules can be applied for building the classier. For that, a specic rule selection strategy needs to be applied, after the total set of class association rules has been determined. It usually aims at selecting the subset with the best predictive power, e. g., using one of the algorithms discussed above in Section 2.

When applying the model, dierent rule combination strategies can be used, e. g., taking the best rule, or aggregating the votes of the individual matching rules, cf. [ 40 ]. Basically, for each rule r that matches an instance i 2 I that we want to classify, we can combine the dierent classications of the individual ri in order to combine the nal classication. The best rule strategy just selects the rule with the highest condence (and its respective classication). In addition, we can apply voting methods for obtaining the nal classication, cf. [ 40 ], i. e., for combining individual predictions as votes for the individual classication. Essentially, for classifying an individual (instance) i 2 I, this works as follows: class(i) = arg max X weight (r) ;

ci2C r2Ri where Ri is the subset of rules matching instance i 2 I of class ci, and C S denotes the set of available classes in our dataset.

The weight of a rule weight (r) depends on the chosen weighting method. Following [ 40 ], we applied the unweighted strategy, where weight U (r) = 1 for all rules r, and the laplacian weight strategy weight L(r) = Laplace(r), where the laplacian weight is determined according to the Laplace correction [ 18 ] to the estimated class probabilities of the applied dataset: (3) (4) Laplace(r) = Pcj2C pjr + jCj

; pir + 1 where pjr (and pir) are the numbers of covered examples by rule r that belong to the respective classes cj (and ci of the rule, respectively). 4

An Adaptive Framework for Class Association Rule Mining In this section, we provide an overview on the proposed approach presenting our novel framework Carma, an Adaptive Framework for Class Association Rule Mining, and provide examples of its instantiation in Section 6. For our adaptive framework, we distinguish two phases: The learning phase that constructs the model, and the classication phase that applies the model.

Learning: Model Construction For the construction of the model, we apply the steps described in Algorithm 1. Basically, Carma starts with discovering class association rules for each class c contained in the dataset. Using subgroup discovery (line 5, calling procedure SubgroupDiscovery that needs to be instantiated with an appropriate subgroup discovery algorithm), we collect a set of class association rules for the specic class, considering a maximal length of the concerned patterns. After that, we apply a boolean ruleset assessment function a (line 6) in order to check, if the quality of the ruleset is good enough. If the outcome of this test is positive, we continue with the next class (line 10). Otherwise, we increase the maximal length of a rule (up to a certain user-denable threshold T , line 12). After the nal set of all class association rules for all classes has been determined, we apply the rule selection function r (line 14) in order to obtain a set of class association rules that optimizes predictive power on the trainingset. That is, the rule selection function aims to estimate classication error and should select the rules according to coverage and accuracy of the rules on the trainingset.

Algorithm 1 CARMA

Require: Set of classes C, k specifying the number of top- k patterns, maxlength T denoting the maximal possible length of a subgroup pattern, quality function q, ruleset assessment function a, rule selection function r. 1: Patterns P = ; 2: for all c 2 C do 3: Current length threshold length = 1 4: while true do 5: Obtain candidate patterns CP by CP = SubgroupDiscovery (DB ; c; S ; q; k ; T ) 6: if Current candidate patterns are good enough, i. e., a(CP ) = true then 7: P = P [ CP 8: break 9: else if length > T then 10: break 11: else 12: length = length + 1 13: Add a default pattern (rule) for the most frequent class to P 14: Apply rule selection function: P = r(P ) 15: return P {Model, consisting of the result set of rules} Classication For the classication phase, we apply all the rules contained in the model P . For aggregating the predictions of the (matching) rules for an individual (instance) i 2 I, and for obtaining the nal classication, we apply a specic rule combination strategy, see Section 3 for examples.

Dataset

We collected a dataset containing a diverse set of activities (classes) split into two categories: (1) Activities which demand the direct usage of the device , e. g., holding the device close to the ear, or putting the device in a specic place, and (2) typical walking activities, e. g., walking slowly or normally. We dened ve scenarios that consist of sets of dierent activities. While doing these activities the person used a smartphone with a running application. This application recorded the sensor data. The persons used the smartphone actively (e. g., putting device in the pocket) or passively (e. g., while walking). Another smartphone was used to record the exact start and nish time of each activity. 39 test persons of dierent sex and age repeated each scenario six times. The resulting dataset consists of a total of 3077 valid single activities. Table 1 shows an overview on the dataset, specic activities and class distributions in detail. Overall, we recorded data from eight dierent sensors, installed on Samsung Galaxy Nexus Device, particularly: (1) Accelerometer, (2) Magnetometer, (3) Gyroscope, (4) Light sensor, (5) Proximity sensor, (6) Rotation vector, (7) Gravity sensor, and (8) Linear acceleration. Using these, we created a set of features applying window-based techniques. A xed window size of 1 second was used. This size was already proven to be ecient for walking activities [ 26 ]. We created 6 features per window and per sensor as described in Table 2. Zerocrossings describes the number of changes from positive to negative and negative to positive values, respectively. The 75th percentile represents the lowest value that is greater than or equal to 75% of the values. Other features were the calculated mean, min/max and standard deviation for the given window. The features were extracted for every axis of every sensor. The only exception were light and proximity sensors. Zero-crossings and the 75th percentile were not calculated for these sensors because of the nature of their returning values. Thus 4 features were obtained for both the light and proximity sensor and 18 for each of the others, resulting in a total set of 116 features. In order to use the features for class association rule mining, we employed the discretization technique by Fayad & Irani [ 22 ] for deriving according selectors. 6

Evaluation

Below, we compare an instantiation of the proposed Carma framework against two baselines: The Ripper algorithm [ 19 ] as a rule-based learner, and the C4.5 algorithm [ 38 ] for learning decision trees. For the subgroup discovery step in the Carma framework, we apply the BSD algorithm [ 33 ] using the implementation provided by the VIKAMINE system [ 9 ]. Further details are described below when we discuss the experimental setup and results.

As the basic evaluation measures, we consider (multi-class) model accuracy and model complexity with respect to activity recognition on the 116 features and 27 classes (shown in Table 1), cf. Section 5. Accuracy is dened as a portion of samples that were classied correctly. Furthermore, complexity relates to the size of a model using two parameters: the total number of rules contained in a rule-based model (also corresponding to the number of leaves in a decision tree), and its average complexity (i. e., for a decision tree the average length of path from a root to a leaf of a tree). All experiments were performed in a standard 10-fold cross-validation setting.

Baselines Results We applied both JRip and J48 algorithms as baseline methods. We compare results with the described approach and explore the inuence of dierent parameters in terms of accuracy and model complexity. When applying the Carma framework, we need to instantiate several components according to the analytical question. In the context of our experiments we instantiate these elements as follows:

For the subgroup discovery algorithm, we selected the BSD algorithm [ 33 ]. For the ruleset assesssment function, we just check, if the median of the rules’ condences is above a certain threshold c. In our experiments, we applied a threshold c = 0:5.

Furthermore, for the rule selection function, we apply an adaptation of the CBA algorithm [ 35 ].

In addition to the basic CBA algorithm, we also implemented a variant, which we call CBA*. This algorithm ensures, that there is at least one rule for each class in the derived model, i. e., when estimating classication performance on the training set, it is checked that at least one rule for each class exists in the nal classier. We default to the rule with the highest condence, if there is none contained in the initial model.

Since we are interested in easily interpretable rules, we also selected the quality function qr (adjusted residuals, described above) which directly maps to signicance criteria.

We opted for interpretable patterns with a maximal length of 7 conditions, and set the respective threshold T = 7 accordingly.

In the evaluation, we used three dierent TopK values: 100, 200 and 500. For the rule combination strategy, we experimented with four strategies: taking the best rule according to condence and Laplace value, the unweighted voting strategy, and the weighted voting (Laplace) method (see Section 3).

Table 4 shows the results of our experiments. Overall, it is easy to see that the proposed approach outperforms the baselines both in accuracy as well as in complexity, i. e., an instantiation with the UnweightedVote or LaplaceVote functions and k = 500 outperforms even the C4.5 baseline clearly. If we especially concentrate on the complexity (or simplicity) of the model, we can observe that Carma demonstrates its advantages since it clearly generates less complex models than the baselines with a comparable accuracy, e. g., C4.5. If we consider the Ripper algorithm, we can observe that it still has a better average complexity (i. e., lower average complexity of a rule) while it outperforms Ripper in terms of accuracy clearly.

Considering the voting functions, we observe that the functions (unweighted voting, and weighted Laplace) always outperform the rest. In our experiments, using larger values of k indicates a higher accuracy here also the compexity (in the number of rules) can be tuned. We observe a slight trade-o between accuracy and complexity here. Basically, the parameter k seems to have an inuence on the complexity, while the remaining instantiations do not seem to have a strong inuence. This can be explained by the fact, that the model generation phase is mainly dependent on k (and the maximum length of the patterns) but not on the applied voting method. CBA and CBA* seem quite close in terms of accuracy and complexity, while we can observe a slight improvement for CBA*. In empirical evaluations it turned out that the dierence between CBA* and CBA was even more pronounced for lower numbers of k, leading to slightly better models for CBA*. However, for our parameter selection, we do not see strong improvements of CBA* compared to CBA.

In summary, the proposed framework always provides a more compact model than the baseline algorithms concerning rule complexity, with simple rules such as: IF minProx = (0:5 3] ^ minMagnetY > 34 ^ zeroCrossAccelX = (0:5 1:5] THEN Class =Hold device near the ear. In our experiments, it is at least in the same range or even better than the baselines concerning accuracy. In particular, considering the best parameter instantiations, the proposed approach is able to outperform both baselines concerning the accuracy (see Figures 1-2).

UnweightedVote LaplaceVote BestLaplace BestConfidence

CBA

J48 JRip Human activity recognition, and interpretable models for classication are prominent research directions, especially considering the ever-increasing amount of available sensor data and social media. In this paper, we presented a unifying view on these topics, proposing a novel approach adaptive class association rule mining using subgroup discovery. We successfully applied and evaluated this approach in the eld of human activity recognition.

UnweightedVote LaplaceVote BestLaplace BestConfidence

CBA*

J48 JRip

The proposed Carma framework is especially suited for generating interpretable rule sets for classication, with a low model complexity. We discussed and analyzed dierent instantiations of Carma, e. g., for parameter selection and for obtaining the nal classier. For our evaluation, we applied real-world data collected for dierent activities using mobile phone sensors. Our experiments showed, that the proposed approach can outperform the baselines clearly, both in terms of accuracy and complexity of the resulting predictive model.

For future work, we aim to consider more datasets, in order to extend the evaluation further. In addition, we aim to analyze the performance of Carma in further domains, e. g., in the medical domain, or for classifying social media. Furthermore, we plan to investigate further rule assessment and rule selection strategies in detail, e. g., [ 36 ], in order to perform further algorithmic comparison and assessment. Based on these, we aim to provide guidelines for instantiating the Carma framework for specic contexts, also in semi-automatic scenarios [ 5 ].

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