scholarly journals Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1971
Author(s):  
Agustin Pérez-Martín ◽  
Agustin Pérez-Torregrosa ◽  
Alejandro Rabasa ◽  
Marta Vaca
Keyword(s):  
Feature Selection ◽  
Financial Institutions ◽  
Credit Scoring ◽  
Feature Selection Method ◽  
Capital Allocation ◽  
Risk Level ◽  
Home Equity ◽  
Home Equity Loans ◽  
The Mean ◽  
Basel Agreement

Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time.

SVM-Based Credit Rating and Feature Selection

2014 ◽  
Vol 618 ◽  
pp. 573-577 ◽  
Author(s):  
Yu Qiang Qin ◽  
Yu Dong Qi ◽  
Hui Ying
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Financial Institutions ◽  
Credit Card ◽  
Credit Rating ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Vector Machines ◽  
Reference Agency

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit rating for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines (SVM) against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.

A straightforward feature selection method based on mean ratio for classifiers

2021 ◽  
pp. 1-12
Author(s):  
Emmanuel Tavares ◽  
Alisson Marques Silva ◽  
Gray Farias Moita ◽  
Rodrigo Tomas Nogueira Cardoso
Keyword(s):  
Feature Selection ◽  
Computational Cost ◽  
Feature Selection Method ◽  
Predictive Ability ◽  
Large Data ◽  
Research Area ◽  
Data Sets ◽  
Promising Alternative ◽  
The Mean ◽  
Mathematical Operations

Feature Selection (FS) is currently a very important and prominent research area. The focus of FS is to identify and to remove irrelevant and redundant features from large data sets in order to reduced processing time and to improve the predictive ability of the algorithms. Thus, this work presents a straightforward and efficient FS method based on the mean ratio of the attributes (features) associated with each class. The proposed filtering method, here called MRFS (Mean Ratio Feature Selection), has only equations with low computational cost and with basic mathematical operations such as addition, division, and comparison. Initially, in the MRFS method, the average from the data sets associated with the different outputs is computed for each attribute. Then, the calculation of the ratio between the averages extracted from each attribute is performed. Finally, the attributes are ordered based on the mean ratio, from the smallest to the largest value. The attributes that have the lowest values are more relevant to the classification algorithms. The proposed method is evaluated and compared with three state-of-the-art methods in classification using four classifiers and ten data sets. Computational experiments and their comparisons against other feature selection methods show that MRFS is accurate and that it is a promising alternative in classification tasks.

A hybrid model with novel feature selection method and enhanced voting method for credit scoring

2021 ◽  
pp. 1-15
Author(s):  
Jianrong Yao ◽  
Zhongyi Wang ◽  
Lu Wang ◽  
Zhebin Zhang ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Subset ◽  
Proposed Model ◽  
Prediction Probability ◽  
Voting Method

With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model.

Securing Debt in an Insecure World

Debtor Nation ◽  
2011 ◽  
Author(s):  
Louis Hyman
Keyword(s):  
Financial Institutions ◽  
Credit Cards ◽  
Home Equity ◽  
Home Equity Loans ◽  
Commercial Loans

This chapter explores how profits on credit cards became the center of lending. By the early 1980s, credit cards metamorphosed from break-even investments to leading earners. With much higher profits than commercial loans, financial institutions began to lend as much money as they could to consumers on credit cards. By the early 1990s, investments in credit cards were twice as profitable as conventional business loans. Increasingly, the now plentiful credit cards allowed consumers to borrow more money and with greater flexibility than they had before. For home owners, home equity loans also offered a new way to borrow by tapping into the value of their homes. Like credit cards, home equity loans allowed borrowers to pay back their debt when they wanted, without a fixed schedule.

scholarly journals A New Feature Selection Algorithm Based on the Mean Impact Variance

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Weidong Cheng ◽  
Tianyang Wang ◽  
Weigang Wen ◽  
Jianyong Li ◽  
Robert X. Gao
Keyword(s):  
Feature Selection ◽  
Back Propagation ◽  
Feature Selection Method ◽  
Principal Component ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bp Network ◽  
The Mean ◽  
New Feature ◽  
Artificial Neural Network Ann

The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN) algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR) method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1) 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2) the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3) 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA) algorithm and a back propagation (BP) network is constructed, and (4) the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates.

Improvement of feature selection method in spam filtering

2009 ◽  
Vol 29 (10) ◽  
pp. 2812-2815
Author(s):  
Yang-zhu LU ◽  
Xin-you ZHANG ◽  
Yu QI
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Spam Filtering

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

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