BACKGROUND Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor...

A new boosting algorithm of Freund and Schapire is used to improve the performance of an ensemble of decision trees which are constructed using the information ratio criterion of Quinlan’s C4.5 algorithm. This boosting algorithm iteratively constructs a series of decision trees, each decision tree being trained and pruned on examples that have been filtered by previously trained trees. Examples...

Key resource page is one of the most important search target pages for Web search users. Decision tree learning is one of the most widely-used and practical methods for inductive inference in machine learning. Because of the difficulty in uniform sampling of Web pages, there are not enough negative instances for training a key resource decision tree. To solve the problem, the original algorithm...

This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy decision tree ensemble customized for each test instance. From the experimental results, we conclude that our boosting-style algorithm significantly improves the performance of the base learner. An empirical comparison...