Control of Variables in Reducts - kNN Classification with Confidence

Ishii, Naohiro; Morioka, Yuichi; Bao, Yongguang; Tanaka, Hidekazu

doi:10.1007/978-3-642-23866-6_11

Cited by 15 publications

(2 citation statements)

References 9 publications

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“…Reduct conception is useful method for the classification. The proposed classification is shown in Fig.1, which consists of some reducts followed by respective Nearest Neighbor(NN) system 10,11 . Nearest neighbor is different from other classification methods such as linear discriminate analysis, neural networks, support vector machine or kernel methods 13 .…”

Section: Generation Of Reducts Based On Nearest Neighbor Relationmentioning

confidence: 99%

“…Then, for the efficient and speedy classification of the parallel processing, nearest neighbor method by Cover and Hart 8 is simple, efficient and speedy with multi-reducts. We propose here a parallel processing scheme of multireducts followed by nearest neighbor classification 10,11,12,13 . Nearest neighbor relation 11,12 between different classes has a b asic information for classification.…”

Section: Introductionmentioning

confidence: 99%

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Generation and Mapping of Multi-Reducts Based on Nearest Neighbor Relation

Ishii¹,

Torii²,

Nakashima³

et al. 2014

IJNDC

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Dimension reduction of data is an important theme in the data processing to represent and manipulate higher dimensional data. Rough set is fundamental and useful to process higher dimensional data. Reduct in the rough set is a minimal subset of features, which has almost the same discernible power as the entire features in the higher dimensional scheme. Combination of multi-reducts is effective for parallel processing of the classification. Nearest neighbor relation between different classes has a b asic information for classification. We propose here a multireduct parallel processing classification scheme with efficient and higher accuracy by using nearest neighbor relation. To improve the classification ability of reducts, we develop a generation method of reducts and its graph mapping method by using the nearest neighbor relation, which is based on characteristics of the weighted reducts for the classification. Further, a dependency relation and an embedding of nearest neighbor relation are proposed to improve the classification accuracy.

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Section: Generation Of Reducts Based On Nearest Neighbor Relationmentioning

confidence: 99%