An Efficient One Class Classifier Using Gaussian-based Hyper-Rectangle Generation

• 발행기관: 한국산업경영시스템학회
• 저자명: Do Gyun Kim(김도균) Jin Young Choi(최진영) Jeonghan Ko(고정한)
• 간행물 정보: 『산업경영시스템학회지』제41권 제2호, 56~64쪽, 전체 9쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2018.06.30

영문 초록

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

목차

1. 서 론

2. 관련 연구 동향

3. 개선된 Hyper-Rectangle 기반 단일 분류기 설계

4. 수치 실험을 통한 분류기 검증

5. 결 론

키워드

Imbalanced Dataset One-Class Classification Classifier Using Hyper-Rectangle Classification Rate Interpretability