The Importance of the Data Complexity Indices on Classification Methods in Data Mining

• Main Authors: Shih Yung Wang, 王詩詠
• Other Authors: 陳景祥
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/79384325767465254236

碩士 === 淡江大學 === 統計學系碩士班 === 101 === Classification techniques in data mining are often used to deal with a variety of classification problems. Choosing suitable methods for analysis from many classification techniques becomes an important issue. For the performance evaluations of the classifiers, researchers used to compare them on several datasets in terms of classification accuracy or training time, and so on. In practice, however, different classification problems has their unique data complexities which might affect the accuracies of the classifiers. Therefore, we adopt fifteen data complexity indices to quantify the data characteristics and use correct classification rate to observe the influence of these indices on seven commonly used classification techniques. We also use factor analysis to explore the correlation among these indices. The results show that different data characteristics indeed have impacts on classification performance. According to our studies, for classification problems, researchers can calculate the data complexity indices or factor values suggested in this paper to estimate the classification difficulties, and also choose the most appropriate classification method on their study.