Feature selection is an important research field for pattern classification, data mining, etc. Population-based optimization algorithms (POA) have high parallelism and are widely used as search algorithm for feature selection. Population-based feature selection algorithms (PFSA) involve compromise between precision and time cost. In order to optimize the PFSA, the feature selection models need to be improved. Feature selection algorithms broadly fall into two categories: the filter model and the wrapper model. The filter model is fast but less precise; while the wrapper model is more precise but generally computationally more intensive. In this paper, we proposed a new mechanism — proportional hybrid mechanism (PHM) to combine the advantages of filter and wrapper models. The mechanism can be applied in PFSA to improve their performance. Genetic algorithm (GA) has been applied in many kinds of feature selection problems as search algorithm because of its high efficiency and implicit parallelism. Therefore, GAs are used in this paper. In order to validate the mechanism, seven datasets from university of California Irvine (UCI) database and artificial toy datasets are tested. The experiments are carried out for different GAs, classifiers, and evaluation criteria, the results show that with the introduction of PHM, the GA-based feature selection algorithm can be improved in both time cost and classification accuracy. Moreover, the comparison of GA-based, PSO-based and some other feature selection algorithms demonstrate that the PHM can be used in other population-based feature selection algorithms and obtain satisfying results.
Improved Relief Weight Feature Selection Algorithm Based on Relief and Mutual Information
