Ensemble GradientBoost for increasing classification accuracy of credit scoring

2017 ◽  
Author(s):  
Armin Lawi ◽  
Firman Aziz ◽  
Syafruddin Syarif
Keyword(s):  
Classification Accuracy ◽  
Credit Scoring

Banking Credit Scoring Assessment Using Predictive K-Nearest Neighbour (PKNN) Classifier

2017 ◽  
pp. 332-350
Author(s):  
Saroj Kanta Jena ◽  
Anil Kumar ◽  
Maheshwar Dwivedy
Keyword(s):  
Service Provider ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Primary Objective ◽  
Scientific Methodology ◽  
Nearest Neighbour ◽  
K Value ◽  
Proposed Model ◽  
Credit Worthiness ◽  
Banking Credit

Credit scoring models is a scientific methodology adopted by credit providers to assess the credit worthiness of applicants. The primary objective of such models has been to predict the potentiality of the loan applicant. A proper evaluation of the credit can help the service provider to determine whether to grant or to reject credit. Therefore, the objective of the study is to predict banking credit scoring assessment using Predictive K-Nearest Neighbour (PKNN) classifier. For the purpose of analysis two different credit approval datasets: Australian credit and German credit have been used. The results from the study show that the proposed model used for classification works better on credit dataset. Here, the study firstly attempted to find the optimal ‘K' value of the neighbourhood so that the classifier is tuned to forecast the credit worthiness and secondly, validated our proposed model on two credit approval datasets by checking the performance of the proposed models on the basis of classification accuracy.

IMPROVING CLASSIFICATION ACCURACY AND CAUSAL KNOWLEDGE FOR BETTER CREDIT DECISIONS

2011 ◽  
Vol 21 (04) ◽  
pp. 297-309 ◽  
Author(s):  
WEI-WEN WU
Keyword(s):  
Classification Accuracy ◽  
Search Algorithm ◽  
Credit Scoring ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Causal Knowledge ◽  
Scoring Model ◽  
Learning Techniques ◽  
Bayesian Network Classifier ◽  
Credit Scoring Model

Numerous studies have contributed to efforts to boost the accuracy of the credit scoring model. Especially interesting are recent studies which have successfully developed the hybrid approach, which advances classification accuracy by combining different machine learning techniques. However, to achieve better credit decisions, it is not enough merely to increase the accuracy of the credit scoring model. It is necessary to conduct meaningful supplementary analyses in order to obtain knowledge of causal relations, particularly in terms of significant conceptual patterns or structures involving attributes used in the credit scoring model. This paper proposes a solution of integrating data preprocessing strategies and the Bayesian network classifier with the tree augmented Na"ıve Bayes search algorithm, in order to improve classification accuracy and to obtain improved knowledge of causal patterns, thus enhancing the validity of credit decisions.

scholarly journals A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set

2008 ◽  
Vol 12 (3) ◽  
Author(s):  
Jozef Zurada ◽  
Peng C. Lam
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Preliminary Investigation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Consumer Loans ◽  
Tree Models ◽  
Accuracy Rates

For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate classification is beneficial to lenders in terms of increased financial profits or reduced losses and to loan applicants who can avoid overcommitment. This paper examines a historical data set from consumer loans issued by a German bank to individuals whom the bank considered to be qualified customers. The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper examines and compares the classification accuracy rates of three decision tree techniques as well as analyzes their ability to generate easy to understand rules.

Using Functional Link Artificial Neural Network (FLANN) for Bank Credit Risk Assessment

2020 ◽  
pp. 1200-1222
Author(s):  
Saroj Kanta Jena ◽  
Maheshwar Dwivedy ◽  
Anil Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Service Provider ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Data Mining Technique ◽  
Functional Link ◽  
Proposed Model ◽  
Artificial Neural ◽  
Banking Credit

Credit scoring is the most important and critical component conducted by the credit providers to decide whether to grant a loan to the applicant or not. Therefore credit scoring models are generally used to predict the potentiality of the loan applicant. A proper evaluation of the credit can help the service provider to determine whether to grant or to reject the credit. The objective of the study is to predict banking credit scoring assessment using a data mining technique i.e. Functional Link Artificial Neural Network (FLANN) classifier. Credit approval datasets: Australian credit and German credit have been used to do this analysis. The output of the study shows that the proposed model used for classification works better on credit dataset. Secondly, we have applied our proposed model on the two credit approval dataset to check the performance of the model for the classification accuracy. A proper evaluation of the credit using the proposed FLANN approach can help the service provider to accurately and quickly ascertain whether to grant credit or to reject.

scholarly journals Does Feature Reduction Help Improve the Classification Accuracy Rates? A Credit Scoring Case Using a German Data Set

2010 ◽  
Vol 14 (2) ◽  
Author(s):  
Jozef Zurada
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Feature Reduction ◽  
Discovery Process ◽  
Data Set ◽  
Independent Features ◽  
Accuracy Rates ◽  
German Data

The paper broadly discusses the data reduction and data transformation issues which are important tasks in the knowledge discovery process and data mining. In general, these activities improve the performance of predictive models. In particular, the paper investigates the effect of feature reduction on classification accuracy rates. A preliminary computer simulation performed on a German data set drawn from the credit scoring context shows mixed results. The six models built on the data set with four independent features perform generally worse than the models created on the same data set with all 20 input features.    

Using Functional Link Artificial Neural Network (FLANN) for Bank Credit Risk Assessment

2017 ◽  
pp. 220-242
Author(s):  
Saroj Kanta Jena ◽  
Maheshwar Dwivedy ◽  
Anil Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Service Provider ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Data Mining Technique ◽  
Functional Link ◽  
Proposed Model ◽  
Artificial Neural ◽  
Banking Credit

Credit scoring is the most important and critical component conducted by the credit providers to decide whether to grant a loan to the applicant or not. Therefore credit scoring models are generally used to predict the potentiality of the loan applicant. A proper evaluation of the credit can help the service provider to determine whether to grant or to reject the credit. The objective of the study is to predict banking credit scoring assessment using a data mining technique i.e. Functional Link Artificial Neural Network (FLANN) classifier. Credit approval datasets: Australian credit and German credit have been used to do this analysis. The output of the study shows that the proposed model used for classification works better on credit dataset. Secondly, we have applied our proposed model on the two credit approval dataset to check the performance of the model for the classification accuracy. A proper evaluation of the credit using the proposed FLANN approach can help the service provider to accurately and quickly ascertain whether to grant credit or to reject.

Classification Accuracy of Sequentially Administered WAIS-IV Short Forms

2011 ◽  
Author(s):  
David S. Kreiner ◽  
Joseph J. Ryan ◽  
Samuel T. Gontkovsky
Keyword(s):  
Classification Accuracy ◽  
Short Forms

Classification accuracy of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2)-Restructured form validity scales in detecting malingered pain-related disability.

2018 ◽  
Vol 30 (7) ◽  
pp. 857-869 ◽  
Author(s):  
Kevin J. Bianchini ◽  
Luis E. Aguerrevere ◽  
Kelly L. Curtis ◽  
Tresa M. Roebuck-Spencer ◽  
F. Charles Frey ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Minnesota Multiphasic Personality Inventory ◽  
Personality Inventory ◽  
Validity Scales

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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