Abstract
When training base classifier by ternary Error Correcting Output Codes (ECOC), it is well know that some classes are ignored. On this account, a non-competent classifier emerges when it classify an instance whose real label does not belong to the meta-subclasses. Meanwhile, the classic ECOC dichotomizers can only produce binary outputs and have no capability of rejection for classification. To overcome the non-competence problem and better model the multi-class problem for reducing the classification cost, we embed reject option to ECOC and present a new variant of ECOC algorithm called as Reject-Option-based Re-encoding ECOC (ROECOC). The cost-sensitive classification model and cost-loss function based on Receiver Operating Characteristic (ROC) curve are built respectively. The optimal reject threshold values are obtained by combing the condition to be met for minimizing the loss function and the ROC convex hull. In so doing, reject option (t1, t2) provides a three-symbol output to make dichotomizers more competent and ROECOC more universal and practical for cost-sensitive classification issue. Experimental results on two kinds of datasets show that our scheme with low-degree freedom of initialized ECOC can effectively enhance accuracy and reduce cost.
Highlights
Uncertainty caused by incomplete data has become a great challenge to the problem of pattern classification [1,2,3,4,5]
From the results in tables, we can see that the classification accuracy got by Re-encoding ECOC (ROECOC) outperform the corresponding stateof-the-art matrices and Re-coding methods most of the time
The multi-class classification aiming at reducing classification cost has been widely used in practice
Summary
Uncertainty caused by incomplete data has become a great challenge to the problem of pattern classification [1,2,3,4,5]. ECOC method effectively reduces a complex multi-class problem into a set of binary problems. ECOC Classification simplifies the complexity of pattern recognition and uses the state-ofthe-art binary classifiers for multi-class classification. There are two steps when using ECOC methods to solve the multi-class issues: the encoding process and the decoding process. Independent of the specific application and the classes used to train dichotomizers, the predefined code ignores the potential information of original classes and confines the improvement of classification accuracy, including oneversus-one matrix, one-versus-all matrix, dense and sparse random matrices. The dichotomies-based code involves finding an optimal code matrix given a set of binary classifiers, proven to be NP-complete by Crammer and Singer [12]
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