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Improved local fisher discriminant analysis based dimensionality reduction for cancer disease prediction
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2020-09-17 , DOI: 10.1007/s12652-020-02542-6
P. N. Senthil Prakash , N. Rajkumar

A good dimensional reduction technique is needed to apply and improve the effectiveness of dimensionality reduction for medical data. High-dimensional data brings great challenges in terms of computational complexity and classification efficiency. It is necessary to compress effectively from high dimensional space to low dimensional space to design a learning curve with good performance. Therefore, dimensional reduction is necessary to study and understand the mechanism of the practical problems of medical data. In this paper, a hybrid local fisher discriminant analysis (HLFDA) method is proposed for the dimension reduction of the medical data. LFDA is a localized variant of Fisher discriminant analysis and it is popular for supervised dimensionality reduction method. The proposed HLFDA is a combination of Locality-preserving projection and LFDA. After the dimensionality reduction process, the data are given to the Type2fuzzy neural network classifier to classify a given data as normal or abnormal. The paper focused on improving performance in terms of prediction accuracy. Three types of UCI cancer dataset is used for analyzing the performance of the proposed method.



中文翻译:

基于改进的局部Fisher判别分析的降维算法可预测癌症

需要一种好的降维技术来应用和提高降维医疗数据的有效性。高维数据在计算复杂度和分类效率方面带来了巨大挑战。必须有效地从高维空间压缩到低维空间,以设计出性能良好的学习曲线。因此,降维是研究和理解医学数据实际问题机理的必要条件。本文提出了一种混合的局部渔民判别分析(HLFDA)方法来减少医学数据的维数。LFDA是Fisher判别分析的本地化变体,在有监督的降维方法中很受欢迎。拟议的HLFDA是保留位置预测和LFDA的结合。在降维处理之后,将数据提供给Type2fuzzy神经网络分类器,以将给定数据分类为正常还是异常。本文着重于提高预测准确性方面的性能。使用三种类型的UCI癌症数据集来分析所提出方法的性能。

更新日期:2020-09-18
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