Abstract: Feature selection is an important problem in the fields of machine learning and pattern recognition. However, in real world domains, the fact that feature selection and feature extraction are not considered together in existing heuristic algorithms leads to the lower efficiency of application system. In this paper, a new feature selection criterion is presented which considers feature selection and feature extraction together. A heuristic algorithm based on information gain and cost of feature extraction evaluation function, ECFS is also given. It is applied to the learning problem in real world domain and is compared with ID3 and BP algorithms. The experimental results show that under the condition of ensuring the recognition rate, ECFS can reduce a lot of cost of feature extraction and improve recognition speed greatly.