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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.19 No.4
- 발행연도
- 2020.12
- 수록면
- 901 - 907 (7page)
- DOI
- 10.7232/iems.2020.19.4.901
이용수
초록· 키워드
오류제보하기
We propose new models and algorithms for automatic classification of objects (clustering) based on the minimum sum-of-squared errors clustering (MSSC) model. Our approach was aimed at improving the accuracy and stability of the result in solving practical problems, such as identifying homogeneous batches of industrial products. We examined the application of the MSSC model and k-means algorithm with various distance measures: Euclidean, Manhattan, Mahalanobis for the problem of automatic classification of objects in a multi-dimensional space of measured parameters (features). For such problems, we present a new model (Mahalanobis Minimum Sum-of-Squared Error Clustering, MMSSC) for solving problems of automatic classification based on the MSSC model with Mahalanobis distance. In addition, we present a new algorithm for automatic classification of objects based on the MMSSC optimization model with the Mahalanobis distance measure and the weighted average covariance matrix calculated from the training sample (pre-labeled data). This algorithm allows us to reduce the number of errors (increasing the Rand index) when identifying homogeneous production batches based on the results of quality control tests. A new approach in the development of evolutionary algorithms for the MSSC problem is presented using a greedy agglomerative heuristic procedure contained in several genetic operators. The use of this approach enables a statistically significant increase in the accuracy of the result (the achieved value of the objective function within the chosen MMSSC mathematical model), as well as its stability, in a fixed time, in comparison with the known algorithms. Thus, in this work, an increase in the accuracy of solving the problem of automatic classification is achieved both by increasing the adequacy of the model (according to the Rand index) and by improving the algorithm that allows us to achieve the best objective function values of within the framework of the chosen model.
목차
ABSTRACT
1. INTRODUCTION
2. CONCLUSION
REFERENCES
참고문헌 (14)
참고문헌 신청
Osman Alp / 2003 / An efficient genetic algorithm for the p-median problem / Annals of Operations Research 122(1/4):21~42
Bozkaya, B. / 2002 / An efficient genetic algorithm for the p-median problem / Facality Location : Applications and Theory 29:179~205
R. De Maesschalck / 2000 / The Mahalanobis distance / Chemometrics and Intelligent Laboratory Systems 50(1):1~18
Daniel Gribel / 2019 / HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering / Pattern Recognition 88:569~583
C. M. Hosage / 1986 / Discrete space location-allocation solutions from genetic algorithms / Annals of Operations Research 6(2):35~46
Bozkaya, B. / 2002 / An efficient genetic algorithm for the p-median problem / Facality Location : Applications and Theory 29:179~205
R. De Maesschalck / 2000 / The Mahalanobis distance / Chemometrics and Intelligent Laboratory Systems 50(1):1~18
Daniel Gribel / 2019 / HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering / Pattern Recognition 88:569~583
C. M. Hosage / 1986 / Discrete space location-allocation solutions from genetic algorithms / Annals of Operations Research 6(2):35~46
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