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Erschienen in:
Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Kaizen Programming for Feature Construction for Classification

verfasst von : Vinícius Veloso de Melo, Wolfgang Banzhaf

Erschienen in: Genetic Programming Theory and Practice XIII

Verlag: Springer International Publishing

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Abstract

A data set for classification is commonly composed of a set of features defining the data space representation and one attribute corresponding to the instances’ class. A classification tool has to discover how to separate classes based on features, but the discovery of useful knowledge may be hampered by inadequate or insufficient features. Pre-processing steps for the automatic construction of new high-level features proposed to discover hidden relationships among features and to improve classification quality. Here we present a new tool for high-level feature construction: Kaizen Programming. This tool can construct many complementary/dependent high-level features simultaneously. We show that our approach outperforms related methods on well-known binary-class medical data sets using a decision-tree classifier, achieving greater accuracy and smaller trees.

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Vorheriges Kapitel Learning Heuristics for Mining RNA Sequence-Structure Motifs
Nächstes Kapitel GP As If You Meant It: An Exercise for Mindful Practice
Fußnoten
1
Thirty-two runs were performed because it is a multiple of 8, and the runs were done in parallel on a quad-core machine with hyper-threading, so we employed all available processing units.
 
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Metadaten
Titel
Kaizen Programming for Feature Construction for Classification
verfasst von
Vinícius Veloso de Melo
Wolfgang Banzhaf
Copyright-Jahr
2016
Buch
Genetic Programming Theory and Practice XIII

Print ISBN: 978-3-319-34221-4

Electronic ISBN: 978-3-319-34223-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-34223-8_3