FeatureSelection:使用不同的算法创建新的特征选择算法-源码

上传者: 42129113 | 上传时间: 2021-07-17 19:43:41 | 文件大小: 392KB | 文件类型: ZIP
功能选择 通过探索不同的算法,创建新的创新特征选择算法。 为了解决特定的问题,应该相应地建立模型。 但是,用于建模的特征选择是一个复杂且耗时的问题,因为很难预测我们需要在问题中查看哪些特征。 特征选择是自动形成与项目最相关的特征子集并准备数据以进行进一步处理的过程。 动机 纯蚁群优化(ACO)能够以惊人的运行时间(比其他搜索方法低)实现高精度解决方案。 但是,纯蚁群优化在特征选择问题上缺少一些特定领域的信息,其目的是在最大程度地提高准确性的同时减少选择特征的数量。 可以将另一种搜索算法放在最上面,从而找到最合适的ACO初始设置。 选择“模拟退火”是因为它在搜索空间中获得了很大的随机性。 工具 Python 2.7 numpy:数学库,对数组执行操作。 熊猫:图书馆,数据处理和分析 scikit-learn:ML库 数据参考 虹膜: : 乳腺癌威斯康星州(诊断): : 社区与

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