Abstract: In view of the low classification accuracy of traditional K-nearest neighbor method for data classification, this paper combines feature selection with KNN classification method and uses improved marine predator algorithm to optimize data features. Firstly, domain learning is used to provide rich neighborhood location information to expand the search range of marine predators, the dimensional variation mechanism is introduced to increase population diversity and avoid falling into local optimum too early, and the sines and cosines disturbance operator and jump step control factor are used to update the location of predators, so as to strengthen the global search and local search capabilities. Secondly, the feature selection object is taken as the optimization object to obtain the selected optimal feature subset. Finally, through to the 14 classic test function optimization test and 14 groups of classic data set classification study, in optimizing the performance, the average number of feature subset and the average classification accuracy comparison research, experimental results show that the proposed algorithm can effectively reduce the redundant features, realize the characteristics of purification, has a broad application prospect in data mining.

Key words: marine predator algorithm, multi-strategy optimization, K-nearest neighbor, feature selection

摘要： 针对传统[K]近邻（K-nearest neighbor）方法用于数据分类存在分类精度低的问题，将特征选择与KNN分类方法结合，并利用改进海洋捕食者算法对数据特征进行优化研究。使用领域学习提供丰富邻域位置信息扩大海洋捕食者的搜索范围，引入维度变异机制增加种群多样性避免过早陷入局部最优，利用正余弦扰动算子和跳跃步长控制因子更新捕食者位置，加强全局搜索和局部搜索能力。将特征选择对象作为优化目标，获得所选的最优特征子集。通过对14个经典测试函数优化测试和14组经典数据集的分类研究，在优化性能、平均特征子集数和平均分类精度进行对比研究，实验结果表明所提算法能够有效降低冗余特征干扰，实现特征提纯，在数据挖掘中具有广阔的应用前景。

关键词: 海洋捕食者算法, 多策略优化, [K]近邻, 特征选择