Abstract
Computational prediction of protein function is an important field in functional genomics. Gene function prediction is a Hierarchical Multi Label Classification (HMC) problem where each gene can belong to more than one functional class simultaneously, while classes are structured in the form of hierarchy. HMC is becoming a necessity in many domains of applications as well. Crowding niching-Adaptive mutation (CAM) is a new proposed method for solving Hierarchical multi-label gene function prediction problem. The classification in CAM-HMC is structured in three different phases. In the first two phases, a sequential procedure is performed. In the first phase, a full cyclic evolutionary crowding algorithm based on new definition of distance between two individuals, and adaptive mutation is applied in order to find classification rules. In the second phase, all the examples that are covered by these rules are removed from the training data. This sequential procedure is repeated until most of the training examples are covered by CAM-HMC rules. In the third phase, consequent generation is determined to show the probability of coverage of each rule for each hierarchical class. Finally, this ratio is applied to classify testing data. Efficiency of this algorithm is displayed by comparing this algorithm with HMC-GA using Precision-Recall curves for three numerical datasets related to protein functions of the Saccharomyces Cerevisiae organism.