Top-down decision tree learning as information based boosting

✍ Scribed by Eiji Takimoto; Akira Maruoka

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 180 KB
• Volume: 292
• Category: Article
• ISSN: 0304-3975
• DOI: 10.1016/s0304-3975(02)00181-0

✦ Synopsis

We consider a boosting technique that can be directly applied to multiclass classiÿcation problems. Although many boosting algorithms have been proposed so far, most of them are developed essentially for binary classiÿcation problems, and in order to handle multiclass classiÿcation problems, they need to be reduced somehow to binary ones. In order to avoid such reductions, we introduce a notion of the pseudo-entropy function G that gives an information-theoretic criterion, called the conditional G-entropy, for measuring the loss of hypotheses. The conditional G-entropy turns out to be useful for deÿning the weakness of hypotheses that approximate, in some way, a multiclass function in general, so that we can consider the boosting problem without reduction. We show that the top-down decision tree learning algorithm using the conditional G-entropy as its splitting criterion is an e cient boosting algorithm. Namely, the algorithm intends to minimize the conditional G-entropy, rather than the classiÿcation error. In the binary case, our algorithm turns out to be identical to the error-based boosting algorithm proposed by Kearns and Mansour, and our analysis gives a simpler proof of their results.