[1]MIN Fan,WANG Hongjie,LIU Fulun,et al.SUCE: semi-supervised binary classification based on clustering ensemble[J].CAAI Transactions on Intelligent Systems,2018,13(6):974-980.[doi:10.11992/tis.201711027]
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SUCE: semi-supervised binary classification based on clustering ensemble
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 6
Page number:
974-980
Column:
学术论文—机器学习
Public date:
2018-10-25
- Title:
- SUCE: semi-supervised binary classification based on clustering ensemble
- Keywords:
- ensemble learning; clustering; clustering ensemble; semi-supervised; binary classification
- CLC:
- TP181
- DOI:
- 10.11992/tis.201711027
- Abstract:
- Semi-supervised learning and ensemble learning are important methods in the field of machine learning. Semi-supervised learning utilize unlabeled samples, while ensemble learning combines multiple weak learners to improve classification accuracy. This paper proposes a new method called Semi-sUpervised classification through Clustering and Ensemble learning (SUCE) for symbolic data. Under different parameter settings, a number of weak learners are generated using multiple clustering algorithms. Using existing class label information the weak learners are evaluated and selected. The test sets are pre-classified by weak learners ensemble. The samples with high confidence are moved to the training set, and the other samples are classified through the extended training set by using the basic algorithms such as ID3, Nave Bayes, kNN, C4.5, OneR, Logistic and so on. The experimental on the UCI datasets results show that SUCE can steadily improve the accuracy of most of the basic algorithms when there are fewer training samples.
- References:
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Last Update:
2018-12-25