Improving Classification Performance of Nursing-Care Text Classification System by Using GA-Based Term Selection
Manabu Nii*,**, Takafumi Yamaguchi*, Yutaka Takahashi*,
Atsuko Uchinuno***, and Reiko Sakashita***
*Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, Japan
**WPI Immunology Frontier Research Center, Osaka University
***College of Nursing Art & Science, University of Hyogo, 13-71 Kitaouji-cho, Akashi, Hyogo, Japan
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.
Atsuko Uchinuno, and Reiko Sakashita, “Improving Classification Performance of Nursing-Care Text Classification System by Using GA-Based Term Selection,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.2, pp. 142-149, 2010.
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