Optimized code matrix generation for classification of multi-class pattern recognition problems using machine learning techniques

摘要

The pattern recognition applications like speech recognition, text classitication and image recognition result in the solution of multi-class problems. Multi-class problems are reduced into several two class problems using the Machine Learning techniques such as Neural Networks and Support Vector Machines. We propose a hybrid approach for the design of output codes for multi-class pattern recognition problems. This approach has the advantage of taking into account the different aspects that are relevant for a code matrix to achieve good performance. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. This model aims at developing a hybrid version of ECOC and adaptive Recursive ECOC with BBO to achieve maximum classification accuracy and minimum computational time. Validation of the results has been performed using non-parametric statistical tests. The statistical results demonstrate that the evolving output codes through BBO provide a general-purpose method for improving the performance of base learners for real world multi-class pattern recognition problems.