Tipawan Silwattananusarn

Tipawan Silwattananusarn, Wanida Kanarkard, Kulthida Tuamsuk

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases: (i) to select feature sets that are likely to be the support vectors by applying ensemble based feature selection methods; and (ii) to construct an SVM ensemble using the selected features. The proposed approach was evaluated by experiments on Cardiotocography dataset. Four feature selection techniques were used: (i) Correlation-based, (ii) Consistency-based, (iii) ReliefF and (iv) Information Gain. Experimental results showed that using the ensemble of Information Gain feature selection and Correlation-based feature selection with SVM ensembles achieved higher classification accuracy than both single SVM classifier and ensemble feature selection with SVM classifier.

Tipawan Silwattananusarn, Pachisa Kulkanjanapiban

The purpose of this paper is to study the patron's usage behavior in an academic library. This study investigates on pattern of patron's books borrowing in Khunying Long Athakravisunthorn Learning Resources Center, Prince of Songkla University that influence patron's academic achievement during on academic year 2015-2018. The study collected and analyzed data from the libraries, registrar, and human resources. The students' performance data was obtained from PSU Student Information System and the rest from ALIST library information system. WEKA was used as the data mining tool employing data mining techniques of association rules and clustering. All data sets were mined and analyzed to identify characteristics of the patron's book borrowing, to discover the association rules of patron's interest, and to analyze the relationships between academic library use and undergraduate students' achievement. The results reveal patterns of patron's book loan behavior, patterns of book usage, patterns of interest rules with respect to patron's interest in book borrowing, and patterns of relationships between patron's borrowing and their grade. The ability to clearly identify and describe library patron's behavior pattern can help library in managing resources and services more effectively. This study provides a sample model as guideline or campus partnerships and for future collaborations that will take advantage of the academic library information and data mining to improve library management and library services.