Journal of Applied Information Science

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Granular Region-Oriented Fuzzy-Rough Based KNN Improvization for Activity Recognition Modeling


Affiliations
1 Department of Information Technology and Engineering, Goa University, Goa, India
     

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Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors (kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvization of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbours of the test object. These neighbor's fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.

Keywords

K-Nearest Neighbors, Human Activity Recognition, Smart Environments, Pervasive Computing, Fuzzy Rough Sets, Fuzzy-Rough Granular Computing.
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  • Granular Region-Oriented Fuzzy-Rough Based KNN Improvization for Activity Recognition Modeling

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Authors

Vijay Borges
Department of Information Technology and Engineering, Goa University, Goa, India
Wilson Jeberson
Department of Information Technology and Engineering, Goa University, Goa, India

Abstract


Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors (kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvization of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbours of the test object. These neighbor's fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.

Keywords


K-Nearest Neighbors, Human Activity Recognition, Smart Environments, Pervasive Computing, Fuzzy Rough Sets, Fuzzy-Rough Granular Computing.

References