CAJ | 학술논문

在特征选择算法中，穷举特征选择算法可选择出最优特征子集，但由于计算量过高而在实际中不可实现。针对计算成本和最优特征子集搜索之间的平衡问题，提出一种新的用于水下目标识别的联合互信息特征选择算法。这个算法的核心思想是：利用顺序向前特征搜索机制，在选择出与类别具有最大互信息特征的条件下，选择具有更多互补分类信息的特征，从而达到快速去除噪声特征和冗余特征及提高识别性能的目的。利用4类实测水下目标数据进行仿真实验，结果表明：在支持向量机识别正确率几乎不变的情况下，联合互信息特征选择方法可以减少87％的特征，分类时间降低58％。与基于支持向量机和遗传算法结合的特征选择方法相比，可以选出更少的特征，特征子集具有更好的泛化性能。
재특정선택산법중，궁거특정선택산법가선택출최우특정자집，단유우계산량과고이재실제중불가실현。침대계산성본화최우특정자집수색지간적평형문제，제출일충신적용우수하목표식별적연합호신식특정선택산법。저개산법적핵심사상시：이용순서향전특정수색궤제，재선택출여유별구유최대호신식특정적조건하，선택구유경다호보분류신식적특정，종이체도쾌속거제조성특정화용여특정급제고식별성능적목적。이용4류실측수하목표수거진행방진실험，결과표명：재지지향량궤식별정학솔궤호불변적정황하，연합호신식특정선택방법가이감소87％적특정，분류시간강저58％。여기우지지향량궤화유전산법결합적특정선택방법상비，가이선출경소적특정，특정자집구유경호적범화성능。
The existing exhaustive feature selection algorithms can select the optimal feature subset of an underwater acoustic target but cannot be used in engineering practices because of their too high computational cost. To balance the computational cost and the optimal feature subset search, we propose what we believe to be a new joint mutual information feature selection (JMIFS) algorithm. Its core consists of: we use the sequence forward feature search mechanism to select the feature that shows the largest amount of mutual information for classification and then select the feature that contributes more mutual information that is complementary to the selected feature so as to remove the noise and redundant features of the underwater acoustic target and enhance the recognition performance. We simu?late the selection of multi?field features of four classes of underwater acoustic targets. The simulation results show preliminarily that: on the condition that the recognition accuracy of the SVM classifier declines only 1%, our JMIFS algorithm can reduce about 87% of the redundant features, and its classification time decreases by 58%. Compared with the SVM and genetic algorithm hybrid feature selection algorithms, the JMIFS algorithm selects a smaller num?ber of feature subsets that have a better generalization performance.