Information quantity-based decision rule acquisition from decision tables

Decision rule acquisition is widely used in data mining and machine learning. In this paper, the limitations of the current approaches to reduct for evaluating decision ability are analyzed deeply. Two concepts, i.e. information entropy and information quantity, and the process of constructing decision tree for acquiring decision rule are introduced. Then, the standard of classical significance measure for selecting attribute is improved, so that the presented approach is aimed at finding a method for rule acquisition without computing relative attribute reduction of a decision table during the process of inducing decision tree and generalizes the rough set-based decision tree construction. The experiment and comparison show that the algorithm provides more precise and simplified decision rules.