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A novel classifier for multivariate instance using graph class signatures

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Abstract

Applications like identifying different customers from their unique buying behaviours, determining ratings of a product given by users based on different sets of features, etc. require classification using class-specific subsets of features. Most of the existing state-of-the-art classifiers for multivariate data use complete feature set for classification regardless of the different class labels. Decision tree classifier can produce class-wise subsets of features. However, none of these classifiers model the relationship between features which may enhance classification accuracy. We call the class-specific subsets of features and the features’ interrelationships as class signatures. In this work, we propose to map the original input space of multivariate data to the feature space characterized by connected graphs as graphs can easily model entities, their attributes, and relationships among attributes. Mostly, entities are modeled using graphs, where graphs occur naturally, for example, chemical compounds. However, graphs do not occur naturally in multivariate data. Thus, extracting class signatures from multivariate data is a challenging task. We propose some feature selection heuristics to obtain class-specific prominent subgraph signatures. We also propose two variants of class signatures based classifier namely: 1) maximum matching signature (gMM), and 2) score and size of matched signatures (gSM). The effectiveness of the proposed approach on real-world and synthetic datasets has been studied and compared with other established classifiers. Experimental results confirm the ascendancy of the proposed class signatures based classifier on most of the datasets.

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Authors and Affiliations

  1. Department of Computer Science, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, 440010, India

    Parnika Paranjape, Meera Dhabu & Parag Deshpande

Authors
  1. Parnika Paranjape
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  2. Meera Dhabu
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  3. Parag Deshpande
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Corresponding author

Correspondence to Parnika Paranjape.

Additional information

Parnika Paranjape received the MTech degree in computer science from Visvesvaraya National Institute of Technology, India, where she is currently pursuing the PhD degree. Her research interests include data mining, graph mining, graph classification, and graph similarity search.

Meera Dhabu received the PhD degree from the Visvesvaraya National Institute of Technology, India. She is currently an assistant professor with the Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology. She has 16 years of academic experience. She has coauthored a number of research articles in various journals, conferences, and book chapters. Her research interests include data mining and machine learning. She is a member of the ACM.

Parag Deshpande received the PhD degree from Nagpur University, India, and the MTech degree from IIT Bombay, India. He is currently a professor with the Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, India. He has 30 years of academic experience. He has co-authored a number of research articles in various journals, conferences, and book chapters. He is the author of several books, including C and Data Structure, Data Warehousing Using Oracle, and Sql/plsql for Oracle 11g (Wiley). His research interests include databases, data mining, and pattern recognition. He is a member of ISTE and SAE-India. He was a recipient of prestigious awards and honors for his excellence in academics and research.

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Paranjape, P., Dhabu, M. & Deshpande, P. A novel classifier for multivariate instance using graph class signatures. Front. Comput. Sci. 14, 144307 (2020). https://doi.org/10.1007/s11704-019-8263-5

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