Error-resilient pattern classification using a combination of spreading and coding gains

Abstract

An approach that aims to enhance error resilience in pattern classification problems is proposed. The new approach combines the spread spectrum technique, specifically its selectivity and sensitivity, with error-correcting output codes (ECOC) for pattern classification. This approach combines both the coding gain of ECOC and the spreading gain of the spread spectrum technique to improve error resilience. ECOC is a well-established technique for general purpose pattern classification, which reduces the multi-class learning problem to an ensemble of two-class problems and uses special codewords to improve the error resilience of pattern classification. The direct sequence code division multiple access (DS-CDMA) technique is a spread spectrum technique that provides high user selectivity and high signal detection sensitivity, resulting in a reliable connection through a noisy radio communication channel shared by multiple users. Using DS-CDMA to spread the codeword, assigned to each pattern class by the ECOC technique, gives codes with coding properties that enable better correction of classification errors than ECOC alone. Results of performance assessment experiments show that the use of DS-CDMA alongside ECOC boosts error-resilience significantly, by yielding better classification accuracy than ECOC by itself.