Improving Learning Efficiency by Attribute Reduction

• Main Authors: Feng-Chun, Lin, 林逢春
• Other Authors: Fengming Michael, Chang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/14553949493535833691

碩士 === 亞洲大學 === 資訊科學與應用學系碩士班 === 97 === For data mining or machine learning, the plethora of parameters that may affect the efficiency of learning, and spend a lot of time to study. Further more not all attributes are important for the data. Rough set theory is an effective tool to reduce the attributes. This study applies the reduced attributes of rough set theory approach. Using the UCI database and example of software ROSE2. 16 set of data were selected to reduce the attributes, and then the artificial neural network, Support vector machine, Bayesian network, ID3 and C4.5 decision tree learning methods are used for comparison. The result show that the application of rough set theory to find the smallest attribute subset for learning, indeed improves the learning efficiency, and accuracy is also improved.