点击次数：

论文类型：会议论文

发表时间：2019-01-01

收录刊物：EI

卷号：2019-July

页面范围：7583-7588

关键字：Feature Extraction; Semantic Interpretation; Axiomatic Fuzzy Set Theory; Machine Learning

摘要：There are many classification tasks in the real world and the digital world. Therefore, classification tasks have always been a hot spot. Nowadays, there are numerous classifiers, including traditional and intelligent methods. Most researchers focus on the accuracy and speed of learned models, they neglected the interpretability and comprehension of the model. In other words, most learned models are like black boxes. i.e., they are not interpretable. However, comprehension and interpretability are very important for a model. To make model interpretable and comprehensible, this paper proposes a new classifier which contains linear discriminant analysis (LDA) feature extraction based on Axiomatic Fuzzy Set (AFS) theory. The proposed method not only get a good performance in the accuracy rate but also has the capability of interpretability and comprehension. Because the proposed method can explore the class by giving semantic descriptions.