Hierarchical error-correcting output codes based on SVDD

Error-correcting output codes (ECOC) can effectively reduce the multiclass to the binary and is attracting close attention, in which the construction of coding matrix based on data is the key to use ECOC to solve multiclass problems. An approach to the hierarchical error-correcting output codes based on support vector data description is presented in this paper. The main idea of the work is to construct the data-driven coding matrix with the help of support vector data description and binary tree. The support vector data description is used to measure the class separability quantitatively to obtain the inter-class separability matrix. And, a binary tree is built based on the matrixes from bottom to top. Then, each node of each layer is encoded to get the final hierarchical error-correcting output code. The independence of base classifiers trained by different encoding methods is compared in experiments. The results show that the proposed technique can promote the diversity of the base classifiers and enhance the classification accuracy.

[1]  Claudio Marrocco,et al.  Design of reject rules for ECOC classification systems , 2012, Pattern Recognit..

[2]  Sergio Escalera,et al.  Re-coding ECOCs without re-training , 2010, Pattern Recognit. Lett..

[3]  Sergio Escalera,et al.  Subclass Problem-Dependent Design for Error-Correcting Output Codes , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  PujolOriol,et al.  Subclass Problem-Dependent Design for Error-Correcting Output Codes , 2008 .

[5]  David Masip,et al.  Online error correcting output codes , 2011, Pattern Recognit. Lett..

[6]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[7]  Nima Hatami,et al.  Thinned-ECOC ensemble based on sequential code shrinking , 2012, Expert Syst. Appl..

[8]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[9]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[10]  Jordi Vitrià,et al.  Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Hui Xue,et al.  Can under-exploited structure of original-classes help ECOC-based multi-class classification? , 2012, Neurocomputing.

[12]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[13]  Nicolás García-Pedrajas,et al.  Improving multiclass pattern recognition by the combination of two strategies , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[15]  Giorgio Valentini,et al.  Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines , 2003, Formal Pattern Analysis & Applications.

[16]  Nicolás García-Pedrajas,et al.  An empirical study of binary classifier fusion methods for multiclass classification , 2011, Inf. Fusion.