A new re-encoding ECOC using reject option
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A new re-encoding ECOC using reject option Lei Lei 1 & Yafei Song 2
&
Xi Luo 1
# The Author(s) 2020
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 metasubclasses. 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. Keywords Error-correcting output codes . Cost-sensitive . Reject option . Receive operating characteristics
1 Introduction Uncertainty caused by incomplete data has become a great challenge to the problem of pattern classification [1–5]. Multi-class classification using Error Correcting Output Codes (ECOC), first proposed by Dietterich and Bakiri [6] in 1995, attracts attention due to its excellent performance. As a decomposition framework, 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. So far, ECOC has been widely applied to spectrum sensing [7], image recognition [8, 9] and disease and fault diagnosis [10, 11]with fairly good classification performance.
* Yafei Song [email protected] 1
College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2
College of Air and Missile Defense, Air Force Engineering University, Xi’an 710051, China
There are two steps when using ECOC methods to solve the multi-class issues: the encoding process and the decoding process. The goal of encoding is to construct a matrix M = (mij)c × l, mij ∈ {1, 0, −1}where rows hold the code words of the class and columns represent bipartitions for the dichotomizers. The classes denoted by zero are ignored in training. The decoding strategy is chosen to merge the outputs of base classifiers. The framework of ECOC is described in Fig. 1: Encoding as the first step is especially crucial. Three main encoding methods are mainly predefined code, datadependent cod
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