Constructing conjunctions using systematic search on decision trees

doi:10.1016/S0950-7051(98)00036-7

Knowledge-Based Systems

Volume 10, Issue 7, May 1998, Pages 421-430

https://doi.org/10.1016/S0950-7051(98)00036-7 Get rights and content

Abstract

This paper investigates a dynamic path-based method for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attribute-value pairs) from paths to form new attributes. Compared with other hypothesis-driven new attribute construction methods, the new idea of this method is that it carries out a systematic search with pruning over each path of a tree to select conditions for generating a conjunction. Therefore, conditions for constructing new attributes are dynamically decided during the search. Empirically, evaluation in a set of artificial and real-world domains shows that the dynamic path-based method can improve the performance of selective decision tree learning in terms of both higher prediction accuracy and lower theory complexity. In addition, it shows some performance advantages over a fixed path-based method and a fixed rule-based method for learning decision trees.

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Constructing conjunctions using systematic search on decision trees

Abstract

Feature construction: an analytic framework and an application to decision trees

Boolean feature discovery in empirical learning

Machine Learning

Constructing conjunctive tests for decision trees

Systematic search for categorical attribute-value data-driven machine learning

Adaptive decision tree algorithms for learning from examples

A scheme for feature construction and a comparison of empirical methods

Constructing new attributes for decision tree learning