Lin Sun

TL;DR: A heuristic feature selection algorithm with low computational complexity is presented to improve the performance of cancer classification using gene expression data and outperforms other related methods in terms of the number of selected genes and the classification accuracy, especially as the size of the genes increases.

TL;DR: The presented model is effective for selecting important features with the higher stability of classification in neighborhood decision systems and the Fisher score model is utilized to delete irrelevant features to decrease the complexity of high-dimensional data sets.

TL;DR: A filter-wrapper preprocessing algorithm for feature selection using the improved Fisher score model is proposed to decrease the spatiotemporal complexity ofMultilabel data, and a heuristic feature selection algorithm is designed for improve classification performance on multilabel datasets.

TL;DR: A novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information.

TL;DR: Experimental results under seven UCI datasets and eight gene expression datasets illustrate that the proposed NMRS-based attribute reduction method using Lebesgue and entropy measures in incomplete neighborhood decision systems is effective to select most relevant attributes with higher classification accuracy, as compared with representative algorithms.