As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. , machine learning and data mining sys­ tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data.

1. Data Processing and KDD.- 1.1 Inductive Learning from Observation.- 1.2 Knowledge Discovery and Data Mining.- 1.3 Feature Selection and Its Roles in KDD.- 1.4 Summary.- References.- 2. Perspectives of Feature Selection.- 2.1 Feature Selection for Classification.- 2.2 A Search Problem.- 2.3 Selection Criteria.- 2.4 Univariate vs. Multivariate Feature Selection.- 2.5 Filter vs. Wrapper Models.- 2.6 A Unified View.- 2.7 Conclusion.- References.- 3. Aspects of Feature Selection.- 3.1 Overview.- 3.2 Basic Feature Generation Schemes.- 3.3 Search Strategies.- 3.4 Evaluation Measures With Examples.- 3.5 Conclusion.- References.- 4. Feature Selection Methods.- 4.1 Representative Feature Selection Algorithms.- 4.2 Employing Feature Selection Methods.- 4.3 Conclusion.- References.- 5. Evaluation and Application.- 5.1 Performance Assessment.- 5.2 Evaluation Methods for Classification.- 5.3 Evaluation of Selected Features.- 5.4 Evaluation: Some Examples.- 5.5 Balance between Different Performance Criteria.- 5.6 Applying Feature Selection Methods.- 5.7 Conclusions.- References.- 6. Feature Transformation and Dimensionality Reduction.- 6.1 Feature Extraction.- 6.2 Feature Construction.- 6.3 Feature Discretization.- 6.4 Beyond the Classification Model.- 6.5 Conclusions.- References.- 7. Less is More.- 7.1 A Look Back.- 7.2 A Glance Ahead.- References.- Appendices.- A-Data Mining and Knowledge Discovery Sources.- A.1 Web Site Links.- A.2 Electronic Newsletters, Pages and Journals.- A.3 Some Publically Available Tools.- B-Data Sets and Software Used in This Book.- B.1 Data Sets.- B.2 Software.- References.

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