Evolutionary Approach to Dimensionality Reduction

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Author(s): Amit Saxena (Guru Ghasida University, Bilaspur, India), Megha Kothari (St. Peter’s University, Chennai, India)and Navneet Pandey (Indian Institute of Technology, Delhi, India)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch125

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Abstract

Excess of data due to different voluminous storage and online devices has become a bottleneck to seek meaningful information therein and we are information wise rich but knowledge wise poor. One of the major problems in extracting knowledge from large databases is the size of dimension i.e. number of features, of databases. More often than not, it is observed that some features do not affect the performance of a classifier. There could be features that are derogatory in nature and degrade the performance of classifiers used subsequently for dimensionality reduction (DR). Thus one can have redundant features, bad features and highly correlated features. Removing such features not only improves the performance of the system but also makes the learning task much simpler. Data mining as a multidisciplinary joint effort from databases, machine learning, and statistics, is championing in turning mountains of data into nuggets (Mitra, Murthy, & Pal, 2002).

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