Abstract
Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk (TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.
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References
QUINLAN J R. C4.5: Programs for machine learning [M]. San Mateo: Morgan Kaufmann Publishers, 1993: 121–145.
SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions [J]. Machine Learning, 1999, 37(3): 297–336.
ZHANG Min-ling, ZHOU Zhi-hua. A lazy learning approach to multi-label learning[J]. Pattern Recognition archive, 2007, 40(7): 2038–2048.
VAPNIK V. The nature of statistical learning theory [M]. New York: Springer-Verlag, 1995:79–92.
JOACHIMS T. Transductive inference for text classification using vector machine [C]// Proc of the 16th International Conference on Machine Learning. Bled, Slovenia, 1999: 200–209.
LIU Yi, JIN Rong, LIU Yang. Semi-supervised multi-label learning by constrained non-negative matrix factorization [C]// Proc of the 21st National Conf on Artificial Intelligence (AAAI’06). Menlo Park, 2006: 421–426.
THORSTEN J. Transductive learning via spectral graph partitioning [C]// Twentieth International Conference on Machine Learning, v1, Ithaca, NY 14853, United States, 2003: 290–297.
ARIK AZRAN. The rendezvous algorithm: Multi-class semi-supervised learning with markov random walks [C]// Twenty-Fourth International Conference on Machine Learning, United Kingdom, 2007 (227): 49–56.
DONATO MALERBA, MICHELANGELO CECI, ANNALISA APPICE. A relational approach to probabilistic classification in a transductive setting [J]. Engineering Applications of Artificial Intelligence, 2009, 22(1): 109–116.
SCHAPIRE R E. The strength of weak learnability [J]. Machine Learning, 1990, 5(2): 97–227.
FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting [J]. Journal of Computer and System Science. 1997, 55(1): 119–139.
ZHOU Zhi-hua. When semi-supervised learning meets ensemble learning [C]// Proceedings of the 8th International Workshop on Multiple Classifier Systems, Nanjing, China. MCS, 2009: 529–538.
QU Yu, SU Hong-ye, GUO Li-chao, CHU Jian. A novel SVM modeling approach for highly imbalanced and overlapping classification [J]. Intelligent Data Analysis, 2011, 15(3): 319–341.
ZHAO Yu-feng, ZHAO Yao, ZHU Zhen-feng. TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation [J]. Expert Systems with Applications, 2009, 36(6): 9813–9818.
MORDECHAI GAL-OR, JERROLD H MAY, WILLIAM E SPANGLER. When to choose an ensemble classifier model for data mining [J]. International Journal of Business Intelligence and Data Mining, 2010, 5(3): 297–318.
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Foundation item: Project(61232001)supported by National Natural Science Foundation of China; Project supported by the Construct Program of the Key Discipline in Hunan Province, China
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Li, H., Lu, Xy., Liu, Ww. et al. Two-way Markov random walk transductive learning algorithm. J. Cent. South Univ. 21, 970–977 (2014). https://doi.org/10.1007/s11771-014-2026-0
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DOI: https://doi.org/10.1007/s11771-014-2026-0