Improve Document Classify Accuracy by Association Rule- Static threshold and Dynamic threshold Research

• Main Authors: Mao-Sheng Hung, 洪茂盛
• Other Authors: Ding-An Chiang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/28707914084041866426

碩士 === 淡江大學 === 資訊工程學系碩士在職專班 === 97 === While using association rule for classification, the experience for association classification rules setting is following single and fixed confidence threshold value, hence is comparatively subjective. In order to increase the accuracy of classification, usually choose higher confidence in accordance with experience, but if set the confidence too high, might cause a part of documentations failed to justify the attributes by lacking rules; if set the confidence too low, it may decrease the documentation classification efficiency. This thesis focus on the threshold value discussion, which divides into two parts, one is static threshold value, though the training process is quicker and simpler, but during the classification procedure, the accuracy that originally already been improved could probably be influenced by follow-up lower confidence rule, namely this kind of confidence rule accuracy is low than the original threshold value setting, therefore may decrease the documentation classification efficiency, so that this thesis proposes the dynamic threshold value, to determine whether the threshold value is upward revision by after each classified whether comparative improved the accuracy or not, also propose in an objective way to set the confidence threshold value to improve the classification efficiency, this thesis proved by experiment the dynamic threshold value can obtain better classification efficiency than static threshold value.