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In this paper, we propose a new clustering-based binary-class classification framework that integrates the clustering technique into a binary-class classification approach to handle the imbalanced data sets. A binary-class classifier is designed to classify a set of data instances into two classes; while the clustering technique partitions the data instances into groups according to their similarity...
Although polarization and spectral information utilization has been received great attention with the sensor and detection technology advance, few results are showed to jointly utilize both of this information in targets classification. Polarization and spectral information reveals two different aspects of one single target, and therefore, if both of information is properly used, good performance...
In this paper, a classification framework is developed to address the issue that empirical determination of the parameters and their values typically makes a classification framework less adaptive and general to different data sets and application domains. Experimental results show that our proposed framework achieves (1) better performance over other comparative supervised classification methods,...
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