Analyzing the Performance of Hierarchical Binary Classifiers for Multi-class Classification Problem Using Biological Data

Multi-class classification problem has become a challenging problem in bioinformatics research. The problem becomes more difficult as the number of classes increases. Decomposing the problem into a set of binary problems can be a good solution in some cases. One of the popular approaches is to build a hierarchical tree structure where a binary classifier is used at each node of the tree. This paper proposes a new greedy technique for building a hierarchical binary classifier to solve multiclass problem. We use neural networks to build all possible binary classifiers and use this greedy strategy to build the hierarchical tree. This technique is evaluated and compared with two popular standard approaches One-Versus-All, One-Versus-One and a multi-class single neural network based classifier. In addition, these techniques are compared with an exhaustive approach that utilizes all possible binary classifiers to analyze how close those classifiers perform to the exhaustive method.