Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Bai, Jian; Xia, Kewen; Lin, Yongliang; Panpan, Wu

doi:10.1155/2017/8986917

Cited by 17 publications

(12 citation statements)

References 20 publications

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“…As is mentioned in the previous section, data elimination is an important process in rough set theory [26]. By using the elimination process, the data becomes less than the original data, but it still gives the same results.…”

Section: B Eliminated Datamentioning

confidence: 98%

Categorizing Attributes in Identifying Learning Style using Rough Set Theory

Sahid¹,

Efendi²,

Hasri³

et al. 2020

IJACSA

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In a learning process, learning style becomes one crucial factor that should be considered. However, it is still challenging to determine the learning style of the student, especially in an online learning activity. Data-driven methods such as artificial intelligence and machine learning are the latest and popular approaches for predicting the learning style. However, these methods involve complex data and attributes. It makes it quite heavy in the computational process. On the other hand, the literate based driven approach has a limitation in inconsistency between results with the learning behavior. Combination, both approaches, gives a better accuracy level. However, it still leaves some issues such as ambiguity and a wide of range of attributes value. These issues can be reduced by finding the right approach and categorization of attributes. Rough set proposed the simple way that can compromise with the ambiguity, vague, and uncertainty. Rough set generated the rules that can be used for prediction or classification decision attributes. Yet, due to the method based on categorical data, it must be careful in determining the category of attributes. Hence, this research investigated several categorizing attributes in the identification learning style. The results showed that the approach gives a better prediction of the learning style. Different categories give different results in terms of accuracy level, number of eliminated data, number of eliminated attributes, and number of generated rules criteria. For decision making, it can be considered by balancing of these criteria.

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Section: B Eliminated Datamentioning

confidence: 98%

Categorizing Attributes in Identifying Learning Style using Rough Set Theory

Sahid¹,

Efendi²,

Hasri³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“… Step 3 . In order to improve the calculation speed and classification accuracy, we use the covering rough set method [ 43 ] to realize the attribute reduction. After attribute reduction, normalization of the actual logging datasets is carried out to avoid computational saturation.…”

Section: Oil Layer Classification Applicationmentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

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Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

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“…There are more than 10 kinds of logging property in Logging series, but some properties are not important, attribute reduction must be carried out to eliminate concentrated redundant attributes in data. We use the attribute reduction based on consistent covering rough set [27].…”

Section: Design Of Oil Layer Recognitionmentioning

confidence: 99%

Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Xia

Niu

et al. 2018

Mathematical Problems in Engineering

Self Cite

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Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate's nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time.

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Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Cited by 17 publications

References 20 publications

Categorizing Attributes in Identifying Learning Style using Rough Set Theory

Categorizing Attributes in Identifying Learning Style using Rough Set Theory

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

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