期刊
NEURAL COMPUTING & APPLICATIONS
卷 25, 期 6, 页码 1303-1311出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-014-1611-3
关键词
Machine learning; Classification; Support vector machine
资金
- Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality [PHR201107123]
- Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation Foundation [20111216N]
- Beijing Municipal Commission of Education, Science and Technology Development [KM201210016014]
- Scientific Research Foundation of Beijing University of Civil Engineering and Architecture [00331609054]
PU classification problem ('P' stands for positive, 'U' stands for unlabeled), which is defined as the training set consists of a collection of positive and unlabeled examples, has become a research hot spot recently. In this paper, we design a new classification algorithm to solve the PU problem: biased twin support vector machine (B-TWSVM). In B-TWSVM, two nonparallel hyperplanes are constructed such that the positive examples can be classified correctly, and the number of unlabeled examples classified as positive is minimized. Moreover, considering that the unlabeled set also contains positive data, different penalty parameters for positive and negative data are allowed in B-TWSVM. Experimental results demonstrate that our method outperforms the state-of-the-art methods in most cases.
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