Cost Matters: A New Example-Dependent Cost-Sensitive Logistic Regression Model

Günnemann, Nikou

doi:10.1007/978-3-319-57454-7_17

Cited by 4 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the issue that traditional cost-sensitive Business failure prediction implemented based on the assumption that misclassification cost matrix is known and fixed, [47] proposed a heterogeneous ensemble selection framework and combined it with a multi-objective optimization algorithm for cost-sensitive business failure prediction, making it adapt to the circumstances when the misclassification cost is uncertain. [48] established an example-dependent cost-sensitive logistic regression algorithm to reduce the misclassification error on minority class samples, thus further saving 10% misclassification cost on a vehicle dataset from a European manufacturer. [49] assigned different costs to each example and proposed an example-dependent cost-sensitive DT to finance-related fields such as credit scoring, fraud detection, and direct marketing, which maximized the cost savings.…”

Section: B Imbalanced Business Failure Predictionmentioning

confidence: 99%

Business Failure Prediction Based on a Cost-Sensitive Extreme Gradient Boosting Machine

Zou

Gao

2022

IEEE Access

View full text Add to dashboard Cite

Business failure prediction is very important for the sustainable development of enterprises. Machine learning algorithms, especially ensemble algorithms, have shown great economic benefits in enterprise financial early warning. However, the highly imbalanced class distribution of financial risk data and the inexplainable of most machine learning-based early distress warning models limit their commercial application. To address the above limitations, we enhance the business failure prediction performance by tree-ensemble in a boosting manner. Moreover, to solve the class imbalanced issue in business failure datasets, a weighted objective function, weighted cross-entropy, is embedded into the boosted tree framework, making the weighted XGBoost a cost-sensitive business failure prediction model. Besides, to tackle the second issue, we explore the intrinsic interpretability of the proposed method by visualizing the feature importance and incorporating a partial dependence plot technique to locally interpret the individual business failure event. Experimental results on business failure datasets with different predictive horizons collected from China Security Market Accounting Research Database (CSMARD) show the proposed weighted XGBoost is a good solution to reduce the error on recognizing firms in business failure. Furthermore, the visualized feature importance score and partial dependence plot result both demonstrate that the cost-sensitive tree-based ensemble can be a good tool to guide the investors in making rational as well as provide interpretable business prediction results as a reference for the policymaking of the regulators.

show abstract

Section: B Imbalanced Business Failure Predictionmentioning

confidence: 99%

Business Failure Prediction Based on a Cost-Sensitive Extreme Gradient Boosting Machine

Zou

Gao

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The MLE principle ensures that the difference between the predicted value and the true value is the smallest; that is, when y i = 1, p i is the largest and when y i = 0, 1 -p i is the largest. The likelihood function for MLE is derived as follows (Günnemann & Pfeffer, 2017).…”

Section: Logistic Regressionmentioning

confidence: 99%

“…As classification errors have different costs, different weights are assigned to different classifications, as shown in Table 2. The weights that classify a positive class as a positive class are c TP , the weights that classify a positive class as a negative class are c FN , the weights that classify a negative class as a positive class are c FP , and the weights that classify a negative class as a negative class are c TN (Günnemann & Pfeffer, 2017).…”

Section: Cost Matrixmentioning

confidence: 99%

“…The right side is the cost-sensitive learner that minimizes the cost of the classification errors by judging both borrowers as possible defaulters so that the resulting classification error cost is 1 + 1 = 2. Therefore, the cost-sensitive classifier has less total cost and is more in line with the actual credit scoring situation (Günnemann & Pfeffer, 2017). Cost-sensitive learning algorithms can be divided into direct and indirect cost-sensitive methods (Ling & Sheng, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Bahnsen, Aouada, and Ottersten (2014) developed an example-dependent cost-sensitive logistic regression model using two publicly available datasets, which achieved high-cost savings compared to the benchmarks; however, the loss function was linear, which caused weak differentiation between the false and correctly classified instances. Günnemann and Pfeffer (2017) introduced a new cost-sensitive prediction model based on a nonlinear loss function, which extended logistic regression and allowed for the different costs of misclassified instances, thereby obtaining prediction results with an overall lower cost. However, most of these cost-sensitive models have been based on a single optimization criterion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Cost-Sensitive Logistic Regression Credit Scoring Model Based on Multi-Objective Optimization Approach

Shen

Wang

Shen

2019

Technological and Economic Development of Economy

View full text Add to dashboard Cite

Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.

show abstract