一种自适应蚁群分类算法

将分类学习看作是一个找出最优分类规则的优化问题，提出一种自适应蚁群分类算法——AdaptiveL_AMP，以得到一组可理解的分类规则.在基于规则的分类方法中，规则评价函数的选取至关重要，本文提出的算法能够针对不同数据集自动选取与之相适应的规则评价函数以提高分类准确性.此外，为进一步提高算法的分类准确率，设计了一种局部搜索策略并将其融入到AdaptiveL_AMP算法中.最后对算法进行了分析，并在多个公用的真实数据集上与相关算法进行了比较，结果表明AdaptiveL_AMP算法能够更加有效地解决分类问题.

An Adaptive Ant Colony Classification Algorithm〓

Taking classification learning as an optimization problem of seeking for optimal classification rules， an adaptive ant colony algorithm， adaptive L_AMP， is proposed to solve classification problem and get the comprehensible classification rules. In the rule based classification methods， the way of choosing of a rule evaluating function is important. The adaptive L_AMP proposed can be used to automatically select an appropriate rule evaluating function according to the data set， thus improving the classification correctness. Moreover， a local search technique is introduced into the algorithm proposed. The algorithm is run on the real data set and compared with other relevant algorithms. The results show the superiority of the algorithm proposed.