A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.

General medical practice in China:evaluation of urban community health care service functions based on a rough set reduction theory

Objective:This study aims to achieve an empirical evaluation on the functional performances of urban community health care services in fi ve administrative districts of Nanchang city in China.Methods:In order to increase effectiveness,data collected from fi ve administrative districts of Nanchang city were processed to exclude redundant information.Rough set reduction theory was brought in to evaluate the performances of community health care services in these districts through calculating key indices’weighed importance.Results:Comprehensive evaluation showed the score rankings from high to low as Qing-yunpu district,Xihu district,Qingshanhu district,Donghu district,and Wanli district.Conclusion:The objective performance evaluation had actually reflected the general situation(including social-economic status)of community health care services in these administrative districts of Nanchang.Attention and practical works of community health service management were needed to build a more harmonious and uniform community health care service system for residents in these districts of Nanchang.

Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

Rough set theory is an important tool to solve uncertain problems. Attribute reduction, as one of the core issues of rough set theory, has been proven to be an effective method for knowledge acquisition. Most of heuristic attribute reduction algorithms usually keep the positive region of a target set unchanged and ignore boundary region information. So, how to acquire knowledge from the boundary region of a target set in a multi-granulation space is an interesting issue. In this paper, a new concept, fuzziness of an approximation set of rough set is put forward firstly. Then the change rules of fuzziness in changing granularity spaces are analyzed. Finally, a new algorithm for attribute reduction based on the fuzziness of 0.5-approximation set is presented. Several experimental results show that the attribute reduction by the proposed method has relative better classification characteristics compared with various classification algorithms.