Applying Genetic Programming in Classification Trees with Multivariate Split Points

• Main Authors: Kuowei Lin, 林國偉
• Other Authors: Chaochang Chiu
• Format: Others
• Language: en_US
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/37160140053070642905

碩士 === 元智大學 === 資訊管理學系 === 90 === In the data mining field, classification is a common and useful technique. It can find the classification rule by learning data from database. The manager can use the rule to decision support. In this paper, propose a methodology different from other classification rule. The proposed methodology is different from using statistic or entropy to build classification tree. Genetic Programming (GP) is an algorithm for searching the optimal. Therefore, we construct the classification tree by GP that search the threshold and feature. The different between our methodology and traditional way, the classification rule contains the non-linear operators (For example: Sin、Cos、Log… ). We apply GP to construct the classification tree with multiple variables. The rule expressed by equation with nonlinear operators. Searching the constants, variable, and threshold through GP, until find an optimal classification tree and then have a perfect accuracy. Besides, it can also deal with the discrete data to improve accuracy. In experiment, our methodology compare with the other methods, it has a good accuracy in some specify datasets.