Combining Classifier Guided by Semi-Supervision

Subject Areas : B. Computer Systems Organization

Mohammad Mohammadi ¹ (Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran)
Hamid Parvin ² (Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran | Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran)
Eshagh Faraji ³ (Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran | Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran)
Sajad Parvin ⁴ (Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran)

Received: 2017-01-04 Accepted : 2017-01-04 Published : 2017-02-01

Keywords: Ensemble Learning, Semi-Supervised Learning, Classifier Ensemble,

Abstract :

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.