Automatic Document Classification Using Multiple Classifiers

• Main Authors: Liang Ching Fu, 梁清福
• Other Authors: Chein-Shung Hwang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/47087767700942760032

碩士 === 中國文化大學 === 資訊管理研究所 === 98 === The development for automatic document classification technology not only can assist massive and repetitive efforts needed in manual classification, but also with the standardization of automatic classification principle and the employment of the repetitive experimentation verification for the algorithms, it can save both the manpower and time cost factors as well. In the advent of internet access and the priority placed on both the knowledge and digital contents either from the personal or enterprise perspective, it results with rather large accumulation of document quantities. Therefore, rapid and effective utilization of information and having them converted into systematic knowledge become increasingly important. This research employs the digital contents processing technology and extends the research findings from other scholars. we combines different classifiers (Bayes classifier,KNN classifier and SVM classifier) to establish a multiple classifiers system for document classification with the aim to obtain better performance. First,a single classifier prototype for each classification algorithm is produced and evaluated by its integral classification performance and individual performance in each category. Then the multiple classifier system combines the results from each single classifier using the voting and the maximum precision schemes. Experimental results show that the multiple classifier system is superior to single classifier in either Macro-F or Macro-F measure.