An experimental analysis of the dependence among codeword bit errors in ecoc learning machines (2003).

Content Provider	CiteSeerX
Author	Masulli, Francesco Valentini, Giorgio
Abstract	One of the main factors a#ecting the e#ectiveness of Error Correcting Output Coding (ECOC) methods for classification is the dependence among the errors of the computed codeword bits. We present an extensive experimental work for evaluating the dependence among output errors of the decomposition unit in ECOC learning machines. In particular, we apply measures based on mutual information to compare the dependence of ECOC Multi-Layer Perceptron (ECOC MLP), made up by a single multi-input multi-output MLP, and ECOC ensembles made up by a set of independent and parallel dichotomizers (ECOC PND). Moreover, the experimentation analyzes the relationship between the architecture, the dependence among output errors and the performances of ECOC learning machines. The results show that the dependence among computed codeword bits is significantly smaller for ECOC PND, pointing out that ensembles of independent parallel dichotomizers are better suited for implementing ECOC classification methods. The experimental results suggest new architectures of ECOC learning machines to improve the independence among output errors and the diversity between base learners.
File Format	PDF
Publisher Date	2003-01-01
Access Restriction	Open
Subject Keyword	Output Error Codeword Bit Error Experimental Analysis Ecoc Learning Machine Computed Codeword Bit Ecoc Pnd Mutual Information Single Multi-input Multi-output Mlp New Architecture Independent Parallel Dichotomizers Ecoc Ensemble Ecoc Mlp Error Correcting Output Coding Main Factor Base Learner Ecoc Classification Method Parallel Dichotomizers Ecoc Multi-layer Perceptron Decomposition Unit Experimental Result Extensive Experimental Work
Content Type	Text