Analyzing the Separability Matrix for ECOC coding
نویسندگان
چکیده
Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Errorcorrecting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding.
منابع مشابه
Separability of ternary codes for sparse designs of error-correcting output codes
Error-correcting output codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. With the extension of the binary ECOC to the ternary ECOC framework, ECOC designs have been proposed in order to better adapt to distributions of the data. In order to decode ternary matrices, recent works redefined many decoding strategie...
متن کاملFacial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers
Within the context face expression classification using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). The method adopted is to train a single error-correcting output code (ECOC) multiclass classifier to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to ...
متن کاملFacial Action Unit Recognition using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classi ers
Within the context face expression classi cation using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). The method adopted is to train a single error-correcting output code (ECOC) multiclass classi er to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to ca...
متن کاملStopping Criteria for Ensemble-Based Feature Selection
Selecting the optimal number of features in a classifier ensemble normally requires a validation set or cross-validation techniques. In this paper, feature ranking is combined with Recursive Feature Elimination (RFE), which is an effective technique for eliminating irrelevant features when the feature dimension is large. Stopping criteria are based on out-of-bootstrap (OOB) estimate and class s...
متن کاملFLIP-ECOC: A Greedy Optimization of the ECOC Matrix
Error Correcting Output Coding (ECOC) is a multiclass classification technique, in which multiple base classifiers (dichotomizers) are trained using subsets of the training data, determined by a preset code matrix. While it is one of the best solutions to multiclass problems, ECOC is suboptimal, as the code matrix and the base classifiers are not learned simultaneously. In this paper, we show a...
متن کامل