Liu H., Motoda H. Feature Selection for Knowledge Discovery and Data Mining

Springer, 1998. — 225 p.As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The computer generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e.g., machine learning and data mining systems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measured in two dimensions, number of features (N) and number of instances (P). Both N and P can be enormously large. This enormity may cause serious problems to many data mining systems.
Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset of features, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates. Simpler data can lead to more concise results and their better comprehensibility. Because of these advantages, feature selection has been the focus of interest for quite some time. Much work has been done from 70's to the present. With the creation of huge databases and the consequent requirements for good data mining programs, new problems arise and novel approaches to feature selection are in high demand. This is a perfect time to look back and see what have been done, and to look forward to the challenges ahead.
This book offers an overview of the various methods developed since 70's, provides a general framework in order to examine many methods and categorize them, employs simple examples to show the essence of representative feature selection methods, compares them using data sets with combinations of intrinsic properties according to the objective of feature selection, suggests guidelines how to use different methods under various circumstances, and points out some new challenges.Data Processing and KDD
Perspectives of Feature Selection
Aspects of Feature Selection
Feature Selection Methods
Evaluation and Application
Feature Transformation and Dimensionality Reduction
Less Is More
A: Data Mining and Knowledge Discovery Sources
B: Data Sets and Software Used in This Book