Applying Kernel Logistic Regression in data mining to classify credit risk

Rahayu, Sri; Purnami, Santi Wulan; Embong, Abdullah

doi:10.1109/itsim.2008.4631725

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…They showed that Logistic regression and J48 algorithms are more efficient algorithms to build classification models for churn analysis. Another work on credit dataset has been done by Embong et al [6] to build a credit risk classification model based on German credit dataset. They compared two well-known classification techniques, kernel logistic regression and support vector machine.…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of fourteen machine learning algorithms on credit card default classification

Altabrawee¹

2018

eduj

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Banks process their financial data by machine learning techniques to get knowledge from the data and use that knowledge in decision making and risk management. In this research, fourteen classification models have been built and trained using a real financial data from a bank in Taiwan. The models forecast the credit card default of a customer which is the repayment delay of the credit granted to the customer. The main idea of the research is evaluating and comparing the models based on their predictive average class accuracy

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Section: Related Workmentioning

confidence: 99%

Performance evaluation of fourteen machine learning algorithms on credit card default classification

Altabrawee¹

2018

eduj

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“…Each cluster was applied different logistic regression method [12], and the results were collected to present a contingency table.…”

Section: Classification Analysismentioning

confidence: 99%

Large Imbalance Data Classification Based on MapReduce for Traffic Accident Prediction

Park

2014

2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

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In modern society, our everyday life has a close connection with traffic issues. One of the most burning issues is about predicting traffic accidents. Predicting accidents on the road can be achieved by classification analysis, a data mining procedure requiring enough data to build a learning model. Regarding building such a predicting system, there are several problems. It requires lots of hardware resources to collect traffic data and analyze it for predicting traffic accidents since the data is very huge. Furthermore, data related to traffic accidents is few comparing with data which is not related to them. The numbers of two types of data are imbalanced. The purpose of this paper is to build a predicting model that can resolve all these problems. This paper suggests using Hadoop framework to process and analyze big traffic data efficiently and a sampling method to resolve the problem of data imbalance. Based on this, the predicting system, first of all, preprocess traffic big data and analyzes it to create data for the learning system. The imbalance of created data is corrected by a sampling method. To improve predicting accuracy, corrected data is classified into several groups, to which classification analysis is applied. These analysis steps are processed by Hadoop framework.

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“…There is a limitation in LR to classify the data with nonlinear boundaries. This problem can be solved using the kernel trick in KLR [20]. This method involves the use of a Kernel function.…”

Section: F Kernal Logistic Regressionmentioning

confidence: 99%

Motor imagery EEG signal classification on DWT and crosscorrelated signal features

Verma

Rao

Sharma

2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

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Motor imagery (MI) based electroencephalogram (EEG) signals are a widely used form of input in brain computer interface systems (BCIs). Although there are a number of ways to classify data, a question still persists as to which technique should be employed in the domain of MI based EEG signals. In this paper, an attempt is made to find the best classification algorithm and feature extraction technique by comparing some of the prominently used algorithms on a same base dataset. Feature extraction techniques like discrete wavelet transform (DWT) and cross-correlation have been studied and compared. Five classification algorithms have been implemented which are logistic regression (LR), kernalised logistic regression (KLR), multilayer perceptron neural network (MLP), probabilistic neural network (PNN) and Least-square support vector machine (LS-SVM). Dataset IVa of BCI competition III has been used as a base dataset to test the algorithms. Evaluation of the algorithms has been done using a 10-fold cross-validation procedure. Experimental results show that a combination of DWT and LSSVM classifier outperforms the other procedures.

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Applying Kernel Logistic Regression in data mining to classify credit risk

Cited by 8 publications

References 17 publications

Performance evaluation of fourteen machine learning algorithms on credit card default classification

Performance evaluation of fourteen machine learning algorithms on credit card default classification

Large Imbalance Data Classification Based on MapReduce for Traffic Accident Prediction

Motor imagery EEG signal classification on DWT and crosscorrelated signal features

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