Numerosity Reduction for Resource Constrained Learning

Computing

Khamisi Kalegele, Research Institute of Electrical Communication, Tohoku University">Hideyuki Takahashi, Johan Sveholm, Research Institute of Electrical Communication, Tohoku University">Kazuto Sasai, Research Institute of Electrical Communication, Tohoku University">Gen Kitagata, Research Institute of Electrical Communication, Tohoku University">Tetsuo Kinoshita

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Keywords: data reduction, agent learning, machine learning, instance ranking

JOURNAL FREE ACCESS

2013 Volume 8 Issue 2 Pages 360-372

DOI https://doi.org/10.11185/imt.8.360

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Abstract

When coupling data mining (DM) and learning agents, one of the crucial challenges is the need for the Knowledge Extraction (KE) process to be lightweight enough so that even resource (e.g., memory, CPU etc.) constrained agents are able to extract knowledge. We propose the Stratified Ordered Selection (SOS) method for achieving lightweight KE using dynamic numerosity reduction of training examples. SOS allows for agents to retrieve different-sized training subsets based on available resources. The method employs ranking-based subset selection using a novel Level Order (LO) ranking scheme. We show representativeness of subsets selected using the proposed method, its noise tolerance nature and ability to preserve KE performance over different reduction levels. When compared to subset selection methods of the same category, the proposed method offers the best trade-off between cost, reduction and the ability to preserve performance.

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