Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

Rodríguez, Jorge; Barrera-Animas, Ari Yair; Trejo, Luis A.; Medina‐Pérez, Miguel Angel; Monroy, Raúl

doi:10.3390/s16101619

Cited by 14 publications

(26 citation statements)

References 24 publications

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“…Using this procedure, a 26-dimensional feature vector is derived; its final structure is shown in Table 2 . This feature vector in the time-domain was used to obtain all results reported in [ 1 , 2 , 12 ]. The subsets NCDS and ACDS of PRIDE are pre-processed using this procedure.…”

Section: Methodsmentioning

confidence: 99%

“…Actually, Barrera-Animas et al reported in [ 1 ] that one-class Support Vector Machine (ocSVM) achieved the best performance among the classifiers used in their experiments. Later on, a new one-class classifier named One-Class K-means with Randomly-projected features Algorithm (OCKRA) proposed by Rodríguez et al in [ 2 ] was introduced for the personal risk detection problem. In their research, they showed that OCKRA achieved the best results in the classification task, leaving ocSVM in second place.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Efficient One-Class Classifier for Mobile Devices and Wearable Sensors on the Context of Personal Risk Detection

Trejo

Barrera-Animas

2018

Sensors

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In this work, we present a first step towards an efficient one-class classifier well suited for mobile devices to be implemented as part of a user application coupled with wearable sensors in the context of personal risk detection. We compared one-class Support Vector Machine (ocSVM) and OCKRA (One-Class K-means with Randomly-projected features Algorithm). Both classifiers were tested using four versions of the publicly available PRIDE (Personal RIsk DEtection) dataset. The first version is the original PRIDE dataset, which is based only on time-domain features. We created a second version that is simply an extension of the original dataset with new attributes in the frequency domain. The other two datasets are a subset of these two versions, after a feature selection procedure based on a correlation matrix analysis followed by a Principal Component Analysis. All experiments were focused on the performance of the classifiers as well as on the execution time during the training and classification processes. Therefore, our goal in this work is twofold: we aim at reducing execution time but at the same time maintaining a good classification performance. Our results show that OCKRA achieved on average, 89.1% of Area Under the Curve (AUC) using the full set of features and 83.7% when trained using a subset of them. Furthermore, regarding execution time, OCKRA reports in the best case a 33.1% gain when using a subset of the feature vector, instead of the full set of features. These results are better than those reported by ocSVM, in which case, even though the AUCs are very close to each other, execution times are significantly higher in all cases, for example, more than 20 h versus less than an hour in the worst-case scenario. Having in mind the trade-off between classification performance and efficiency, our results support the choice of OCKRA as our best candidate so far for a mobile implementation where less processing and memory resources are at hand. OCKRA reports a very encouraging speed-up without sacrificing the classifier performance when using the PRIDE dataset based only on time-domain attributes after a feature selection procedure.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards an Efficient One-Class Classifier for Mobile Devices and Wearable Sensors on the Context of Personal Risk Detection

Trejo

Barrera-Animas

2018

Sensors

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“…The first dataset is generally regarded as wearable sensor data, which are generated locally at the user; the second type represents the sensing data, which are obtained remotely within a short distance. They have wide applications, which have been reported recently, such as [26,27,28] for wearable sensors and [29,30,31] for Kinect-based sensors.…”

Section: Methodsmentioning

confidence: 99%

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Fong

Song

Cho

et al. 2017

Sensors

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In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

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“…Different classifiers in the ensemble are usually created by using a different selection of objects, as is the case of Valentini and Dietterich's multiple ocSVM classifiers (Valentini & Dietterich, ), structured one‐class classification (Wang et al, ; Sharma et al, ), Bagging‐TPMiner (Medina‐Pérez et al, ), and Giacinto et al's modular ensemble (Giacinto et al, , ). Another popular alternative is to create the classifiers in the ensemble by using a different selection of attributes, for example, one attribute per classifier (Juszczak & Duin, ) or subsets of original or derived attributes (Nanni, ; Biggio et al, ; Cheplygina & Tax, ; Krawczyk, ; Rodríguez et al, ; Tax & Duin, ).…”

Section: Related Workmentioning

confidence: 99%

“…We have observed the performance of a one‐class classifier called OCKRA (Rodríguez et al, ), which uses an ensemble of one‐class k ‐means, constructed using random attribute selection. OCKRA was applied to the field of masquerade detection, using the so‐called file system approach (Camiña et al, ), and it showed an increase in performance over other one‐class classifiers.…”

Section: Introductionmentioning

confidence: 99%

Cluster validation in clustering‐based one‐class classification

Rodríguez¹,

Monroy²,

Medina‐Pérez³

et al. 2019

Expert Systems

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Reconstruction‐based one‐class classification has shown to be very effective in a number of domains. This approach works by attempting to capture the underlying structure of the normal class, typically, by means of clusters of objects. It has the main disadvantage, however, that one has to indicate the number of clusters in advance, for this yields an efficient way of computing a clustering. In this paper, we introduce a new algorithm, OCKRA++, which achieves a better performance, by enhancing a clustering‐based one‐class ensemble classifier (OCKRA) with a cluster validity index that is used to set the best number of clusters during the classifier's training process. We have thoroughly tested OCKRA++ in a particular domain, namely masquerade detection. For this purpose, we have used the Windows‐Users and ‐Intruder simulation Logs data set repository, which contains 70 different masquerade data sets. We have found that OCKRA++ is currently the algorithm that achieves the best area under the curve, with a significant difference, in masquerade detection using the file system navigation approach.

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Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

Cited by 14 publications

References 24 publications

Towards an Efficient One-Class Classifier for Mobile Devices and Wearable Sensors on the Context of Personal Risk Detection

Towards an Efficient One-Class Classifier for Mobile Devices and Wearable Sensors on the Context of Personal Risk Detection

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Cluster validation in clustering‐based one‐class classification

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