Key Issues On Pattern Classification On Of Boolean Vector Data And TCM Diagnosis Scale Development

Some key issues of pattern classification for Boolean vector data are put forward and studied in the dissertation, and research results are applied into TCM diagnosis scale development. These issues include similarity measure, dimension reduction and feature weighting. This research would lay a preliminary work for the further study on the pattern classification for Boolean vector data. The main works and key innovations are summarized as the following:1. Similarity coefficients (SC), SC families and their properties are summarized, based on which some important properties of those SC are analyzed. And multi-parameters SC families and its optimization method are proposed for SC optimization. Experiment of actual data show that the proposed SC families are efficient in the SC optimization for Boolean vector classification.2. Dimension reduction methods for Boolean vector data are studied from such two aspects as feature extraction and feature selection. Fisrt, considering about feature extraction, a new method is put forward and studied which based on piecewise summing. Theoretical analysis and experiment results illustrate the efficiency of the method. Second, considering about feature selection, the filter and hybrid feature selection algorithms are proposed which based on SC in order to solve such different problems as mutually exclusive biclassification, non-mutually exclusive multi-classification. Experiment results illustrate that the efficiencies of these methods.3. Based on the review of the existing feature weight methods, some improved methods for the k-NN feature weight algorithm are proposed in order to solve the problem that the speed of the traditional k-NN is very slow. And considering about medicine diagnosis test, an improved method is proposed to solve the Fisher linear descriminant algorithm for the calculated of the algorithm's threshold. Theoretical analysis and experiment results illustrate that the above two improved methods are efficient than the corresponding traditional methods.4. Methods proposed in the dissertation are applied into developing the TCM (Traditional Chinese Medicine) diagnosis scale for stroke syndromes, which are used to resolve the item selection and item weight problems in scale development.