The design of soft recoding-based strategies for improving error-correcting output codes

Many Error-Correcting Output Codes (ECOC) algorithms had been proposed based on the hard coding (HC) schemes: binary coding {1, 0} or ternary coding {+1, -1, 0}. This paper introduces two novel strategies to recode the original code matrices with the mean values and the intervals of learners' outputs, which are named Mean Value Recoding (MVR) and Interval Recoding (IR) strategies. Both strategies are designed to reduce the distance between the outputs of base learners and the target codewords, aiming to produce more accurate results compared with the HC schemes. It is the first time that two concepts, soft recoding, and learner dependent, are injected into the ECOC framework to the best of our knowledge. To verify the effectiveness of our strategies, four data-independent ECOC algorithms and two data-dependent ECOC algorithms are deployed in the experiments based on UCI data sets. The experiments are carried out using the original HC strategies and our soft recoding strategies, and results verify that our strategies outperform the HC-based algorithms in most cases by producing balanced results among classes. In short, our strategies can improve the performance of different ECOC algorithms. Our python code and the corresponding data sets are available for non-commercial or research use at: https://github.com/MLDMXM2017/softcoding-ECOC.