Nfs 2 se dashboard not appearing11/13/2022 ![]() Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2020) Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems. Fund Inf 53(3-4):365–390Ĭhen M, Wu KS, Chen XH, Tang CH (2014) An entropy-based uncertainty measurement approach in neighborhood systems. Slezak D (2002) Approximate entropy reducts. Pawlak Z, Skowron A (2007) Rough sets and Boolean reasoning. In: 17Th international conference on machine learning, Morgan Kaufmann, San Franciscoĭash M, Liu H (2003) Consistency-based search in feature selection. Hall M (2000) Correlation-based feature selection for discrete and numeric class machine learning. Artif Intell 97(1-2):273–324įreeman C, Kulic D, Basir O (2015) An evaluation of classifier-specific filter measure performance for feature selection. Kohavi R, John GH (1997) Wrappers for feature subset selection. Qian YH, Liang XY, Wang Q, Liang JY, Liu B, Skowron A, Yao YY, Ma JM, Dang CY (2018) Local rough sets: a solution to rough data analysis in big data. Pattern Recogn 67:410–423ĭas AK, Das S, Ghosh A (2017) Ensemble feature selection using bi-objective genetic algorithm. Li F, Miao DQ, Pedrycz W (2017) Granular multi-label feature selection based on mutual information. Experimental results over different real-life data sets have verified the feasibility and efficiency of the proposed algorithm from the perspective of the runtime. Finally, an efficient incremental feature selection algorithm for selecting a new feature subset is developed when deleting and adding a feature set simultaneously. When new features may appear while old features are deleted, the updated neighborhood entropy is computed incrementally to reflect the significance of mixed-type features, which is an important step in the dynamic feature selection process. On this basis, the neighborhood entropy is given to evaluate the uncertainty of the mixed-type data. ![]() At first, the hybrid relation is given to define the similarity between objects for the mixed-type data without resorting to the discretization process. In this study, we focus on the feature selection process for mixed-type data under the variation of feature set by the utilization of neighborhood rough sets. The neighborhood rough set model has attracted much attention to select a feature subset when handling with mixed-type data. ![]() Feature selection from mixed-type data has attracted considerable research attention. In many real-world applications, mixed-type data including missing, numerical, and categorical features are ubiquitous in medical treatment, intrusion detection, traffic analysis and so on. Feature selection is to find relevant features and delete redundant features, which provides a basis for classification problems. ![]()
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