基于實(shí)例的模式識(shí)別中，由于存在大量實(shí)例和特征個(gè)數(shù)可變特性，導(dǎo)致分類器識(shí)別性能低下，難于形成具有高度概括性的共性對(duì)象實(shí)例?；诿庖哂?jì)算的概念提取是在有效降低特征個(gè)數(shù)的同時(shí)，提取各類的中心，以此為實(shí)例模式對(duì)待識(shí)別樣本進(jìn)行分類決策。實(shí)驗(yàn)結(jié)果表明算法在保持甚至提高分類精度的同時(shí)，不僅有效地降低了特征個(gè)數(shù)，而且提取的類中心分類效果更好。與基于遺傳算法的概念提取結(jié)果相比較，在有限代數(shù)內(nèi)，該算法能收斂到更優(yōu)的類中心，從而驗(yàn)證了算法的有效性及其應(yīng)用潛力。
關(guān)鍵詞 免疫計(jì)算；概念提取；特征選擇；模式分類

Studies on the Method of Concept Extraction Based on the Immune Computation Liu Jing Zhao Yougang Han Zhongzhi Abstract In the pattern recognition based on examples, the large amounts of examples and the variation of the features cause the low recognition performance of the classifier and make it difficult 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.
Keywords immune computation; concept extraction; feature selection; pattern
classification