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KNN也称作K最邻近结点算法(k-Nearest Neighbor algorithm)的缩写形式,是分类预测的经典算法之一。在煤炭勘探过程中,往往希望将新勘探出来的煤炭产品分类预测,并采取对应类别的煤炭处理和保存措施。文中,将采用基于特征选择的KNN分类算法应用到煤炭勘探分类预测工作中,该方法首先选择煤炭分类中有价值的特征属性,并使用KNN算法完成分类工作.该算法可以有效地提高煤炭分类预测工作的效率,并且有助于煤炭后续的保存和精加工措施。
KNN, also known as K-Nearest Neighbor Algorithm, is one of the classical algorithms for classification prediction. In the process of coal exploration, it is often desirable to classify newly-identified coal products and forecast the corresponding types of coal handling and preservation measures. In this paper, KNN classification algorithm based on feature selection is applied to coal prospecting classification and forecasting. Firstly, valuable attribute in coal classification is selected and classified by KNN algorithm. This algorithm can effectively improve the coal classification prediction Work efficiency, and contribute to the follow-up of coal preservation and finishing measures.