Abstract

This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree. Contents 1 Introduction 1 2 Design Goals 1 3 An Improved Algorithm 2 3.1 Incorporating a Training Instance : : : : : : : : : : : : : : : : : : : : : : : : 2 3.2 Ensuring a Best Test at Each Decision Node : : : : : : : : : : : : : : : : : : 3 3.3 Information Kept at a Decision Node : : : : : : : : : : : : : : : : : : : : : : 3 3.4 Tree Transposition : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 3.5 Slewing a Cutpoint : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 3.6 How to Ensure a Best Test Everywhere : : : : : : : : : : : : : : : : : : : : : 5 4 Incremental Training Cost 5 5 Error-Correction Mo...

Citations

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