A novel dimensionality reduction method for cancer dataset using PCA and Feature Ranking

In data mining, a well known problem of “Curse of Dimensionality” occurs due to presence of large number of dimensions in a dataset. This problem leads to reduced accuracy of machine learning classifiers because of presence of many insignificant and irrelevant dimensions or features in the dataset. Data mining applications such as bioinformatics, risk management, forensics etc., generally involves very high dimensionality. There are many methods that are being used to reduce dimensionality and find Critical Dimensions that represents complete dataset but using lesser dimensions. This paper introduces a novel method to reduce dimensionality using Principal Component Analysis and Feature Ranking. For analysis of proposed method, dimensionality reduction of Breast Cancer dataset has been done. The results indicate that for the chosen dataset, proposed method can effectively reduce dimensionality without compromising on classification accuracy and computation cost.