An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

Sun, Lin; Zhang, Xiaoyu; Xu, Jiucheng; Zhang, Shiguang

doi:10.3390/e21020155

Cited by 30 publications

(17 citation statements)

References 54 publications

(75 reference statements)

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“…However, the accuracy is affected in complex datasets. To improve the classification performance of complex data, (Sun et al, 2019c) introduced an attribute reduction method utilizing neighborhood entropy measures. The systems should have the ability to handle continuous data while maintaining its information on attribute classification.…”

Section: Related Workmentioning

confidence: 99%

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Reddy

Chittineni

2021

PeerJ Computer Science

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Information efficiency is gaining more importance in the development as well as application sectors of information technology. Data mining is a computer-assisted process of massive data investigation that extracts meaningful information from the datasets. The mined information is used in decision-making to understand the behavior of each attribute. Therefore, a new classification algorithm is introduced in this paper to improve information management. The classical C4.5 decision tree approach is combined with the Selfish Herd Optimization (SHO) algorithm to tune the gain of given datasets. The optimal weights for the information gain will be updated based on SHO. Further, the dataset is partitioned into two classes based on quadratic entropy calculation and information gain. Decision tree gain optimization is the main aim of our proposed C4.5-SHO method. The robustness of the proposed method is evaluated on various datasets and compared with classifiers, such as ID3 and CART. The accuracy and area under the receiver operating characteristic curve parameters are estimated and compared with existing algorithms like ant colony optimization, particle swarm optimization and cuckoo search.

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Section: Related Workmentioning

confidence: 99%

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Reddy

Chittineni

2021

PeerJ Computer Science

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“…Attribute reduction of an information system often starts from the calculation of the attribute set to save much time (Min and Liu, 2009;Sun et al, 2017) since positive threshold change based on the global similarity relation is irregular; therefore, it is advisable to add the current foremost attribute from the empty set. The initial range of system threshold is [τ, 0.99] with threshold reduced by 0.01 from 0.99 every time to calculate the degree of dependence of the condition attribute set on the decision attribute set till the degree of dependence is unchanged (Sun et al, 2019). Input: information system S (U, C∪D, V, f ), and the number of condition attributes is n Output: reduced set of the attribute of S…”

Section: Attribute Reduction Algorithm Based On the Tolerance Relationmentioning

confidence: 99%

Application of Rough Set and Neural Network in Water Energy Utilization

et al. 2021

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In water energy utilization, the damage of fault occurring in the power unit operational process to equipment directly affects the safety of the unit and efficiency of water power conversion and utilization, so fault diagnosis of water power unit equipment is especially important. This work combines a rough set and artificial neural network and uses it in fault diagnosis of hydraulic turbine conversion, puts forward rough set theory based on the tolerance relation and defines similarity relation between samples for the decision-making system whose attribute values are consecutive real numbers, and provides an attribute-reducing algorithm by making use of the condition that approximation classified quality will not change. The diagnostic rate of artificial neural networks based on a rough set is higher than that of the general three-layer back-propagation(BP) neural network, and the training time is also shortened. But, the network topology of an adaptive neural-fuzzy inference system is simpler than that of a neural network based on the rough set, the diagnostic accuracy is also higher, and the training time required under the same error condition is shorter. This algorithm processes consecutive failure data of the hydraulic turbine set, which has avoided data discretization, and this indicates that the algorithm is effective and reliable.

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“…C and D are, respectively, called the condition attribute and the decision attribute sets. For two subsets of attributes in decision table, the input features form the set C while the class indices are D. Let I be a subset of A, the equivalence relation [26] I ND(I) is denoted as follows.…”

Section: Multi-reductionmentioning

confidence: 99%

A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

et al. 2019

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Revealing brain functional connection pathways is of great significance in understanding the cognitive mechanism of the brain. In this paper, we present a novel rough set based dynamic multi-reduction algorithm (DMRA) to analyze brain functional connection pathways. First, a binary discernibility matrix is introduced to obtain a reduction, and a reduction equivalence theorem is proposed and proved to verify the feasibility of reduction algorithm. Based on this idea, we propose a dynamic single-reduction algorithm (DSRA) to obtain a seed reduction, in which two dynamical acceleration mechanisms are presented to reduce the size of the binary discernibility matrix dynamically. Then, the dynamic multi-reduction algorithm is proposed, and multi-reductions can be obtained by replacing the non-core attributes in seed reduction. Comparative performance experiments were carried out on the UCI datasets to illustrate the superiority of DMRA in execution time and classification accuracy. A memory cognitive experiment was designed and three brain functional connection pathways were successfully obtained from brain functional Magnetic Resonance Imaging (fMRI) by employing the proposed DMRA. The theoretical and empirical results both illustrate the potentials of DMRA for brain functional connection pathways analysis.

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An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

Cited by 30 publications

References 54 publications

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Application of Rough Set and Neural Network in Water Energy Utilization

A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

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