Self-Adaptive Weighting Text Association Categorization Algorithm Research
In text association classification research, feature distribution of the training sample collection impacts greatly on the classification results, even with a same classification algorithm classification results will have obvious differences using different sample collections. In order to solve the problem, the stability of association classification is improved by the weighing method in the paper, the design realizes the association classification algorithms (WARC) based on rule weight. In the WARC algorithm, this paper proposes the concept of classification rule intensity and gives the concrete formula. Using rule intensity defines the rule adjustment factors that adjust uneven classification rules. Experimental results show the accuracy of text classification can be improved obviously by self-adaptive weighting.
Zhihua Xu, Gang Shen and Sally Lin
L. J. Li et al., "Self-Adaptive Weighting Text Association Categorization Algorithm Research", Advanced Materials Research, Vols. 171-172, pp. 246-251, 2011