Addressing imbalanced data with argument based rule learning

Napierała, Krystyna; Stefanowski, Jerzy

doi:10.1016/j.eswa.2015.07.076

Cited by 24 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The three types of resampling techniques are oversampling, undersampling, and a hybrid of the two (Guo et al ., 2017). In terms of computational time, the undersampling method is the best method for big data sets (Loyola‐González, Martínez‐Trinidad, Carrasco‐Ochoa, & García‐Borroto, 2016; Napierała & Stefanowski, 2015), as it reduces the number of samples from the majority class. We utilize this method for the preprocessing part of our model.…”

Section: Methodsmentioning

confidence: 99%

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

Babaei

Bamdad

2020

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

Summary This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.

show abstract

Section: Methodsmentioning

confidence: 99%

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

Babaei

Bamdad

2020

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

show abstract

“…The problem of the imbalanced data set in the P2P lending market has been mentioned in many studies in the past years (Namvar et al., 2018; Cho et al., 2019). Considering the computational time, the undersampling method is the best method for big data sets (Napierała and Stefanowski, 2015; Loyola‐González et al., 2016). An undersampling approach eliminates the records of the majority class randomly (Bastani et al., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Application of credit‐scoring methods in a decision support system of investment for peer‐to‐peer lending

Babaei

Bamdad

2021

Int Trans Operational Res

View full text Add to dashboard Cite

Peer-to-peer lending, as a novel lending model, has challenged investors to make effective investment decisions. Issued loans are grouped into default and nondefault. Therefore, different classification methods can be utilized to predict the status of loans in the future. Our study aim is to propose an investment decision model based on the nondefault loans predicted using three different classifiers, including random forest (RF) that is a multitude of decision trees, support vector machine, and naïve Bayes. In fact, we combine each of these classifiers with the portfolio optimization problem to understand which combination leads to the best portfolio concerning the risk and return. In order to find the best combined model, numerical studies are conducted based on real-world data. In addition, the performances of these classifiers are evaluated based on different performance measures using the 10-fold cross-validation method. The results indicate that combining the RF algorithm and portfolio optimization problem leads to the least level of risk. Moreover, it is concluded that RF is the best classifier in terms of performance analysis.

show abstract

“…Castro et al [21] proposed a cost-sensitive algorithm (CSMLP) using a single cost parameter to differentiate misclassification errors to improve the performances of MLPs on binary imbalanced class distributions. ABMODLEM [33] addressed imbalanced data with argument based rule learning. By using the expert knowledge, CSMLP and ABMODLEM improve learning rules from imbalanced data.…”

Section: Literature Reviews a Cost-sensitive Learningmentioning

confidence: 99%

A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Zhang

Tan

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

228

View full text Add to dashboard Cite

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on the training data that presents an effective approach to incorporating the evaluation measure (i.e., G-mean) into the objective function. We first optimize the misclassification costs, and then apply them to DBN. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state of the art on both benchmark data sets and real-world data set for fault diagnosis in tool condition monitoring.

show abstract

Addressing imbalanced data with argument based rule learning

Cited by 24 publications

References 37 publications

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

Application of credit‐scoring methods in a decision support system of investment for peer‐to‐peer lending

A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Contact Info

Product

Resources

About