A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation

Dahiya, Shashi; Handa, S. S.; Singh, N P

doi:10.14569/ijarai.2016.050901

Cited by 14 publications

(10 citation statements)

References 27 publications

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“…Few recent works using correlation filtering in feature selection include (Dahiya et al. 2016 ; Hsu and Hsieh 2010 ; Low et al. 2016 ; Kim and Chung 2017 ; Zou et al.…”

Section: Methodsmentioning

confidence: 99%

“…Feature-class correlation and Feature-feature correlation help to identify the important features. Few recent works using correlation filtering in feature selection include (Dahiya et al 2016;Hsu and Hsieh 2010;Low et al 2016;Kim and Chung 2017;Zou et al 2016;Xu et al 2016;Senawi et al 2017). Low et al (2016), Kim and Chung (2017), Zou et al (2016), Xu et al (2016) and Senawi et al (2017) are tested on biomedical datasets.…”

Section: Ranking Metricsmentioning

confidence: 99%

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A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Nagarajan

Babu

2021

Netw Model Anal Health Inform Bioinforma

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Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

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“…Few recent works using correlation filtering in feature selection include (Dahiya et al. 2016 ; Hsu and Hsieh 2010 ; Low et al. 2016 ; Kim and Chung 2017 ; Zou et al.…”

Section: Methodsmentioning

confidence: 99%

Section: Ranking Metricsmentioning

confidence: 99%

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Nagarajan

Babu

2021

Netw Model Anal Health Inform Bioinforma

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“…, 2009; Chong, 2013) with multiple layers between the input and output layers. MLP is widely used for credit risk assessment (Dahiya et al. , 2016; Luo, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…MLP is one of the artificial neural network techniques, which has good capability of approximating any finite sets of real numbers (Juhos et al, 2009;Chong, 2013) with multiple layers between the input and output layers. MLP is widely used for credit risk assessment (Dahiya et al, 2016;Luo, 2019). The two hidden-layered MLP is deemed to have better performance (Juhos et al, 2009;Chester, 1990), and it non-linearizes several linear regression models by the typical sigmoid activation function:…”

Section: Random Forest Regression (Rf)mentioning

confidence: 99%

Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

Gao

Liu

et al. 2021

APJML

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PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

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“…To compare, our proposed approach with EMFFS, four feature selection methods were combined, namely, chi-square, information gain, Pearson correlation and gain ratio using Weka tool. Finally, we combined the output of these four feature selection schemes using a fusion based rank aggregation method proposed in [ 73 ] to generate a final global features ranking list.…”

Section: 0 Significance Of Dataset and Proposed Feature Setsmentioning

confidence: 99%

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

et al. 2017

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ObjectivesWidespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models.MethodsAccident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.ResultsRandom forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.ConclusionThe proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

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A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation

Cited by 14 publications

References 27 publications

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

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