Decision tree-based classifier incorporating contrast patterns
Hiroyuki Morita, Takanobu Nakahara, Yukinobu Hamuro, Shoji Yamamoto · 2009
In the last ten years, studies that focus on the extraction of patterns among contrast classes and reveal the differences among these classes have been conducted. There exist two major streams in such studies, namely, emerging patterns (EPs) and contrast patterns (CPs), and related works concerning both have been proposed. In this field of study, the main problems pertain to extracting the efficiency of EPs or CPs and constructing smart classifiers on their basis. In this study, we propose a decision tree-based classifier using contrast patterns extracted by LCM. We also propose a method to construct a decision tree model that incorporates contrast patterns. Contrast patterns are extracted by LCM efficiency, and diverse scenarios are indicated by decision tree models in terms of business applications. Further, an example of our studies is illustrated using practical case data.