Instance-based classification using prototypes generated from large noisy and streaming datasets

Stefanos Ougiaroglou1, 2, Dimitris A. Dervos1 and Georgios Evangelidis2

  1. Department of Information and Electronic Engineering, International Hellenic
    University, GR-57400, Sindos, Thessaloniki, Greece
    stoug@uom.edu.gr,dad@it.teithe.gr
  2. Department of Applied Informatics, School of Information Sciences, University of Macedonia
    156 Egnatia Str., GR-54006, Thessaloniki, Greece
    gevan@uom.gr

Abstract

Nowadays, large volumes of training data are available from various data sources and streaming environments. Instance-based classifiers perform adequately when they use only a small subset of such datasets. Larger data volumes introduce high computational cost that prohibits the timely execution of the classification process. Conventional prototype selection and generation algorithms are also inappropriate for data streams and large datasets. In the past, we proposed prototype generation algorithms that maintain a dynamic set of prototypes and are appropriate for such types of data. Dynamic because existing prototypes may be updated, or new prototypes may be appended to the set of prototypes in the course of processing. Still, repetitive generation of new prototypes may result to forming unpredictably large sets of prototypes. In this paper, we propose a new variation of our algorithm that maintains the prototypes in a convenient and manageable way. This is achieved by removing the weakest prototype when a new prototype is generated. The new algorithm has been tested on several datasets. The experimental results reveal that it is as accurate as its predecessor, yet it is more efficient and noise tolerant.

Key words

k-NN classification, Data reduction, Prototype generation, Data streams, Large datasets, Noisy data

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS190518044O

Publication information

Volume 17, Issue 1 (January 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Ougiaroglou, S., Dervos, D. A., Evangelidis, G.: Instance-based classification using prototypes generated from large noisy and streaming datasets. Computer Science and Information Systems, Vol. 17, No. 1, 71-92. (2020), https://doi.org/10.2298/CSIS190518044O