基于免疫计算的概念提取方法研究

Studies on the Method of Concept Extraction Based on the Immune Computation

基于实例的模式识别中,由于存在大量实例和特征个数可变特性,导致分类器识别性能低下,难于形成具有高度概括性的共性对象实例。基于免疫计算的概念提取是在有效降低特征个数的同时,提取各类的中心,以此为实例模式对待识别样本进行分类决策。实验结果表明算法在保持甚至提高分类精度的同时,不仅有效地降低了特征个数,而且提取的类中心分类效果更好。与基于遗传算法的概念提取结果相比较,在有限代数内,该算法能收敛到更优的类中心,从而验证了算法的有效性及其应用潜力。

In the pattern recognition based on examples, the large low recognition performance of the classifier and make it difficult amounts of examples and the variation of the features cause the to draw the succinct generalization of the general examples instance. The concept extraction based on the immune computation is to lower effectively the amount of marks and to extract the center of every class which is used in the classifying of the samples to be classified. The result of the experiment indicates that the algorithm can keep up and even enhance the precision of classification, lower effectively the amount of features and supply a good effect of classifying based on the center of classes. Compared with the result of concept extraction based on the genetic algorithm, this one can converge more effectively to the center of classes in the finite algebra, thus verify the effectiveness and potential application of this algorithm.