BOAT -- Optimistic Decision Tree Construction

BOAT -- Optimistic Decision Tree Construction (1999)

by Johannes Gehrke , Venkatesh Ganti , Raghu Ramakrishnan , Wei-Yin Loh

Citations:

97 - 1 self

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Datum	Value	Source
TITLE	BOAT -- Optimistic Decision Tree Construction	user correction
AUTHOR NAME	Johannes Gehrke	user correction
AUTHOR NAME	Venkatesh Ganti	user correction
AUTHOR NAME	Raghu Ramakrishnan	user correction
AUTHOR NAME	Wei-Yin Loh	user correction
AUTHOR AFFIL	Department of Computer Sciences and Department of Statistics, University of Wisconsin-Madison	user correction
ABSTRACT	Classification is an important data mining problem. Given a training database of records, each tagged with a class label, the goal of classification is to build a concise model that can be used to predict the class label of future, unlabeled records. A very popular class of classifiers are decision trees. All current algorithms to construct decision trees, including all main-memory algorithms, make one scan over the training database per level of the tree. We introduce a new algorithm (BOAT) for decision tree construction that improves upon earlier algorithms in both performance and functionality. BOAT constructs several levels of the tree in only two scans over the training database, resulting in an average performance gain of 300% over previous work. The key to this performance improvement is a novel optimistic approach to tree construction in which we construct an initial tree using a small subset of the data and refine it to arrive at the final tree. We guarantee that any differen...	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
CITATIONS	23 found	ParsCit 1.0