Attribute reduction approaches for general relation decision systems

Liu, Guilong; Li, Ling; Yang, Jitao; Feng, Yunhe; Zhu, Kai

doi:10.1016/j.patrec.2015.06.031

Cited by 24 publications

(2 citation statements)

References 24 publications

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“…Definition 10. [34,35] In the neighborhood decision system NDS = (U, A, d), ∀B ⊆ A, ∀a ∈ A, then the neighborhood importance degree B (a The eigenvector corresponding to the upper and lower approximations of d are as follows:…”

Section: Reduction Of Neighborhood Rough Setmentioning

confidence: 99%

Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Gao¹,

Lv²,

Wu³

2020

IJCIS

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Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix. In this paper, we rewrite neighborhood approximation sets in the matrix-based form. Based on the matrix-based neighborhood approximation sets, we propose the relative dependency degree of attributes and the corresponding algorithm (DBM). Furthermore, we design the reduction algorithm (ARNI) for continuous value information systems. Compared with other algorithms, ARNI can effectively remove redundant attributes, and less affect the classification accuracy. On the other hand, the experiment shows ARNI based on the matrixing rough set model can significantly speed up by GPU. The speedup is many times over the central processing unit implementation.

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Section: Reduction Of Neighborhood Rough Setmentioning

confidence: 99%

Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Gao¹,

Lv²,

Wu³

2020

IJCIS

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show abstract

“…These techniques are especially important for datasets with tens or hundreds of thousands of features. That is why extensive research from various points of view has been made in recent years for fast and efficient attribute reduction algorithms (e.g., Szemenyei and Vajda, 2017;Liu et al, 2015;Martinović et al, 2014;Sun et al, 2014;Min et al, 2014;Borowik and Łuba, 2014;Korzen and Jaroszewicz, 2005;Zhong and Skowron, 2001;Liu and Setiono, 1997;Łuba and Rybnik, 1992).…”

Section: Introductionmentioning

confidence: 99%

Optimization on the Complementation Procedure Towards Efficient Implementation of the Index Generation Function

Borowik

2018

International Journal of Applied Mathematics and Computer Science

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In the era of big data, solutions are desired that would be capable of efficient data reduction. This paper presents a summary of research on an algorithm for complementation of a Boolean function which is fundamental for logic synthesis and data mining. Successively, the existing problems and their proposed solutions are examined, including the analysis of current implementations of the algorithm. Then, methods to speed up the computation process and efficient parallel implementation of the algorithm are shown; they include optimization of data representation, recursive decomposition, merging, and removal of redundant data. Besides the discussion of computational complexity, the paper compares the processing times of the proposed solution with those for the well-known analysis and data mining systems. Although the presented idea is focused on searching for all possible solutions, it can be restricted to finding just those of the smallest size. Both approaches are of great application potential, including proving mathematical theorems, logic synthesis, especially index generation functions, or data processing and mining such as feature selection, data discretization, rule generation, etc. The problem considered is NP-hard, and it is easy to point to examples that are not solvable within the expected amount of time. However, the solution allows the barrier of computations to be moved one step further. For example, the unique algorithm can calculate, as the only one at the moment, all minimal sets of features for few standard benchmarks. Unlike many existing methods, the algorithm additionally works with undetermined values. The result of this research is an easily extendable experimental software that is the fastest among the tested solutions and the data mining systems.

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