Data Mining with Decision Trees―Theroy and Applications

Rokach (Ben-Gurion U.) and Maimon (Tel-Aviv U.) believe decision trees, already popular due to their success and simplicity, do a great job of predicting and explaining the relationship between some measurements about an item and its target value and also offer such benefits as versatility and high performance. Here they give students and professionals what they need to grow decision trees, evaluate classification trees, split criteria, "prune" trees, create advanced decision trees and forests, manage incremental learning of decisions trees, select features, make fuzzy trees, combine decision trees with other techniques and classify sequences. Their text is accessible and loaded with examples, and their bibliography is especially comprehensive. Annotation c2008 Book News, Inc., Portland, OR (booknews.com)