CAJ | 학술논문

针对知识库的不完备所导致的分类器泛化能力较差的问题，提出一种在先验知识引导下基于遗传算法的知识发现方法。该方法通过引入问题近似先验领域知识，进行种群初始化和变异函数构造，利用先验知识引导下的遗传算法对问题的解空间进行搜索，最终获取新知识。利用该方法可以获得同时覆盖先验领域知识和训练样例的一般知识，进而提高分类器的分类性能和泛化能力。实验结果表明，与经典遗传算法相比，不仅该算法的泛化能力更强，而且所获得特征规模较小。
침대지식고적불완비소도치적분류기범화능력교차적문제，제출일충재선험지식인도하기우유전산법적지식발현방법。해방법통과인입문제근사선험영역지식，진행충군초시화화변이함수구조，이용선험지식인도하적유전산법대문제적해공간진행수색，최종획취신지식。이용해방법가이획득동시복개선험영역지식화훈련양례적일반지식，진이제고분류기적분류성능화범화능력。실험결과표명，여경전유전산법상비，불부해산법적범화능력경강，이차소획득특정규모교소。
Incomplete knowledge base leads to the problem of poor generalisation ability of the classifier.Aiming at this issue,we put for-ward a genetic algorithm knowledge-based discovery method guided by priori knowledge.The method initialise the population and construct the mutation function by introducing the approximate prior domain knowledge of the problem,makes use of the genetic algorithm guide by pri-ori knowledge to search the solution space of the problem,finally obtains new knowledge.Using this method,it is able to get the general knowledge covering the priori domain knowledge and training examples simultaneously,thereby improves the classification performance and generalisation ability of the classifier.Experimental results show that,compared with classical genetic algorithm,the algorithm has better gen-eralisation ability,and the features obtained is also smaller in size.