Improving Classification Performance of Nursing-Care Text Classification System by Using GA-Based Term Selection
Manabu Nii, Takafumi N. Yamaguchi, Yutaka Takahashi, Atsuko Uchinuno, Reiko Sakashita · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2010
In order to reduce evaluation workloads for nursingcare experts, we have proposed a Support Vector Machine (SVM) based classification system. In this paper, for improving the classification performance, we propose a Genetic Algorithm (GA) based attribute selection method. First, we extract nouns and verbs from nursing-care texts by using of the morphological analysis software and store the extracted terms into a “term list.” Next, some combinations of terms in the term list are selected by a GA with two objectives; (1) maximizing the number of correctly classified texts and (2) minimizing the number of selected terms. Then, we classify the nursing-care texts with these selected terms by using of a SVM-based classification system. From computer simulations, we show the effectiveness of a GA-based attribute selection method for classifying the nursing-care texts.