원문정보
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초록
영어
A bottle up hyperbox granular computing (HBGrC) is developed based on distance measure. Firstly, hyperbox granule is represented by the beginning point and the end point. Secondly, the distance measure between two hyperbox granules is defined by the beginning points and the end points. Thirdly, operations between two hyperbox granules are designed to the transformation between two hyperbox granule spaces with different granularities, HBGrC is developed by the join operator and the user-defined granularity threshold on the basis of bottle up scheme. Experimental results shown that HBGrC achieved the better testing accuracies over the machine learning benchmark datasets.
목차
Abstract
1. Introduction
2. Motivation and Related Work
2.1. Motivation
2.2. Related Work
3. Hyperbox Granular Computing Based Distance Measure
3.1. Representation and Granularity for the Hyperbox Granule
3.2. Distance Measure
3.3. Operations between Two Hyperbox Granules
3.4. The Hyperbox Granular Computing Based on Distance Measure
4. Experiments
4.1. Classification in 2-dimensional Space
4.2. Classification in N-dimensional Space
5. Conclusion
References
1. Introduction
2. Motivation and Related Work
2.1. Motivation
2.2. Related Work
3. Hyperbox Granular Computing Based Distance Measure
3.1. Representation and Granularity for the Hyperbox Granule
3.2. Distance Measure
3.3. Operations between Two Hyperbox Granules
3.4. The Hyperbox Granular Computing Based on Distance Measure
4. Experiments
4.1. Classification in 2-dimensional Space
4.2. Classification in N-dimensional Space
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보