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Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

2005

Feature selection

Articles 1 - 3 of 3

Full-Text Articles in Physical Sciences and Mathematics

Selection Of Independent Binary Features Using Probabilities: An Example From Veterinary Medicine, Ludmila I. Kuncheva, Zoë S.J. Hoare, Peter D. Cockcroft Nov 2005

Selection Of Independent Binary Features Using Probabilities: An Example From Veterinary Medicine, Ludmila I. Kuncheva, Zoë S.J. Hoare, Peter D. Cockcroft

Journal of Modern Applied Statistical Methods

Supervised classification into c mutually exclusive classes based on n binary features is considered. The only information available is an n×c table with probabilities. Knowing that the best d features are not the d best, simulations were run for 4 feature selection methods and an application to diagnosing BSE in cattle and Scrapie in sheep is presented.

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Comparing High-Order Boolean Features, Adam Drake, Dan A. Ventura Jul 2005

Comparing High-Order Boolean Features, Adam Drake, Dan A. Ventura

Faculty Publications

Many learning algorithms attempt, either explicitly or implicitly, to discover useful high-order features. When considering all possible functions that could be encountered, no particular type of high-order feature should be more useful than any other. However, this paper presents arguments and empirical results that suggest that for the learning problems typically encountered in practice, some high-order features may be more useful than others.

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Predictive Neural Networks For Gene Expression Data Analysis, Ah-Hwee Tan, Hong Pan Apr 2005

Predictive Neural Networks For Gene Expression Data Analysis, Ah-Hwee Tan, Hong Pan

Research Collection School Of Computing and Information Systems

Gene expression data generated by DNA microarray experiments have provided a vast resource for medical diagnosis and disease understanding. Most prior work in analyzing gene expression data, however, focuses on predictive performance but not so much on deriving human understandable knowledge. This paper presents a systematic approach for learning and extracting rule-based knowledge from gene expression data. A class of predictive self-organizing networks known as Adaptive Resonance Associative Map (ARAM) is used for modelling gene expression data, whose learned knowledge can be transformed into a set of symbolic IF-THEN rules for interpretation. For dimensionality reduction, we illustrate how the system …

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