Combining Classifier Guided by Semi-Supervision

Journal of Advances in Computer Research

مقاله 3، دوره 8، شماره 1 - شماره پیاپی 27، اردیبهشت 2017، صفحه 27-50   

نوع مقاله: Original Manuscript

نویسندگان

Mohammad Mohammadi¹؛ Hamid Parvin^{* 1، 2}؛ Eshagh Faraji^{1، 2}؛ Sajad Parvin¹

¹Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

²Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

چکیده

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high-dimensional feature-space via an unsupervised learning including an attribute discrimination component. The unsupervised clustering component assigns degree of typicality to each data pattern in order to identify and reduce the effect of noisy or outlaid data patterns. Then, the suggested technique obtains the best combination parameters for each background. The experimentations on artificial datasets and standard SONAR dataset demonstrate that our classifier ensemble does better than individual classifiers in the ensemble.

کلیدواژه‌ها

Semi-Supervised Learning؛ Ensemble Learning؛ Classifier Ensemble