The Effect Of Small Disjuncts And Class Distribution On Decision Tree Learning

The Effect Of Small Disjuncts And Class Distribution On Decision Tree Learning (2003)

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by Gary Mitchell Weiss

Venue:	RUTGERS UNIVERSITY
Citations:	3 - 0 self

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Datum	Value	Source
TITLE	The Effect of Small Disjuncts and Class Distribution on Decision	SVM HeaderParse 0.1
AUTHOR NAME	Gary Mitchell Weiss	SVM HeaderParse 0.1
AUTHOR AFFIL	Tree Learning	SVM HeaderParse 0.2
AUTHOR NAME	Haym Hirsh	SVM HeaderParse 0.1
AUTHOR ADDR	� 2003	SVM HeaderParse 0.1
AUTHOR NAME	Gary Mitchell Weiss	SVM HeaderParse 0.1
AUTHOR AFFIL	Tree Learning	SVM HeaderParse 0.2
AUTHOR NAME	Gary Mitchell Weiss	SVM HeaderParse 0.1
AUTHOR AFFIL	Tree Learning	SVM HeaderParse 0.2
AUTHOR NAME	Dissertation Director	SVM HeaderParse 0.1
AUTHOR NAME	Haym Hirsh	SVM HeaderParse 0.1
AUTHOR ADDR	� 2003	SVM HeaderParse 0.2
ABSTRACT	The main goal of classifier learning is to generate a model that makes few misclassifica-tion errors. Given this emphasis on error minimization, it makes sense to try to under-stand how the induction process gives rise to classifiers that make errors and whether we can identify those parts of the classifier that generate most of the errors. In this thesis we provide the first comprehensive studies of two major sources of classification errors. The first study concerns small disjuncts, which are those disjuncts within a classifier that cover only a few training examples. An analysis of classifiers induced from thirty data sets shows that these small disjuncts are extremely error prone and often account for the majority of all classification errors. Because small disjuncts largely determine classifier performance, we use them as a "lens " through which to study classifier induction. Factors such as pruning, training-set size, noise and class imbalance are each analyzed to deter-mine how they affect small disjuncts and, more generally, classifier learning. The second study analyzes the effect that rare classes and class distribution have on learning. Those examples belonging to rare classes are shown to be misclassified much more often than common classes. The thesis then goes on to analyze the impact that	SVM HeaderParse 0.1
CITATIONS	65 found	ParsCit 1.0