A new rule-based knowledge extraction approach for imbalanced datasets

Mahani, Aouatef; Baba-Ali, Ahmed Riadh

doi:10.1007/s10115-019-01330-9

Cited by 8 publications

(4 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments on several stateof-the-art ensemble models are performed to verify the effectiveness of the EGHE model. ey are an EMPNGAbased multistage hybrid model put forward by Zhang and Xia [37]; the heterogeneous ensemble credit model put forward by Xia et al [40]; EBCA-RF&XGB-PSO model that is put forward by He et al [41]; heterogeneous ensemble learning-based two-stage credit risk model (TSHE) proposed by Papouskova and Hajek [42]; twin neural networks (TNN) proposed by Jayadeva et al [43]; and a new rule-based knowledge extraction (RKE) method proposed by Mahani and Baba [44] recently. Table 8 gives the results of ensemble models in different data sets.…”

Section: Experimental Results Different Methods Are Utilized As Comparison Models To Test the Validity Of The Eghe Credit Scoring Modelmentioning

confidence: 99%

A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Zhang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit scoring model perform more effectively, which comprises three major stages. Firstly, it conducts preprocessing for collected credit data. Then, an efficient feature selection algorithm based on adaptive elastic net is employed to reduce the weakly related or uncorrelated variables to get high-quality training data. Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier. Finally, a new weighting method for single ELM classifiers in the ensemble model is established with respect to their classification accuracy based on generalized fuzzy soft sets (GFSS) theory. A novel cosine-based distance measurement algorithm of GFSS is also proposed to calculate the weights of each ELM classifier. To confirm the efficiency of the proposed ensemble credit scoring model, we implemented experiments with real-world credit data sets for comparison. The process of analysis, outcomes, and mathematical tests proved that the proposed model is capable of improving the effectiveness of classification in average accuracy, area under the curve (AUC), H-measure, and Brier’s score compared to all other single classifiers and ensemble approaches.

show abstract

Section: Experimental Results Different Methods Are Utilized As Comparison Models To Test the Validity Of The Eghe Credit Scoring Modelmentioning

confidence: 99%

A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Zhang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The formal background is an important component of formal concept analysis. At present, FCA has been widely applied in fields such as knowledge discovery [10] , rule extraction [11] , and software engineering [12] . This paper combines formal concept analysis with neural networks, combining the advantages of extracting text information from formal background and digital information from neural networks, and proposes a new algorithm for crime prediction.…”

Section: Related Workmentioning

confidence: 99%

FC-NT: a crime prediction algorithm incorporating formal context and neural networks

Zao,

Lv,

Ding

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model.

show abstract

“…This approach [87] applies an intelligent method to the extraction of the classification rules from imbalanced binary datasets based on three phases.…”

Section: Undersampling By Genetic Algorithm (Usga)mentioning

confidence: 99%