Classifying Categorical Data by Rule-Based Neighbors

Jiabing Wang, Pei Zhang, Guihua Wen, Jia Wei

Abstract: A new learning algorithm for categorical data, named CRN (Classification by Rule-based Neighbors) is proposed in this paper. CRN is a nonmetric and parameter-free classifier, and can be regarded as a hybrid of rule induction and instance-based learning. Based on a new measure of attributes quality and the separate-and-conquer strategy, CRN learns a collection of feature sets such that for each pair of instances belonging to different classes, there is a feature set on which the two instances disagree. For an unlabeled instance I and a labeled instance I', I' is a neighbor of I if and only if they agree on all attributes of a feature set. Then, CRN classifies an unlabeled instance I based on I's neighbors on those learned feature sets. To validate the performance of CRN, CRN is compared with six state-of-the-art classifiers on twenty-four datasets. Experimental results demonstrate that although the underlying idea of CRN is simple, the predictive accuracy of CRN is comparable to or better than that of the state-of-the-art classifiers on most datasets.