Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach

• URL: http://arxiv.org/abs/2412.04183v1
• Date: Thu, 05 Dec 2024 14:21:18 GMT
• Title: Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach
• Authors: Md Shihab Reza, Monirul Islam Mahmud, Ifti Azad Abeer, Nova Ahmed,
• Abstract summary: We implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity. Our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. To interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global)
• Score: 9.88281854509076
• License:
• Abstract: The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As features of the dataset are a crucial factor for the credit scoring system, we implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity. We compared 6 different machine learning models, 1 deep learning model, and a hybrid model with and without using LDA. From the result, we have found our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. Lastly, to interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global). Through this research, we showed how feature reduction techniques can be used without affecting the performance and explainability of the model, which can be very useful in resource-constrained settings to optimize the computational workload.

Related papers

• Green LIME: Improving AI Explainability through Design of Experiments [44.99833362998488]
  Local Interpretable Model-agnostic Explanations (LIME) provides explanations by generating new data points near the instance of interest and passing them through the model. LIME is highly versatile and can be applied to a wide range of models and datasets. By utilizing optimal design of experiments' techniques, we reduce the number of function evaluations of the complex model.
  arXiv  Detail & Related papers  (2025-02-18T11:15:04Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model [8.06111903129142]
  This paper proposes a miniblock diagonal hessian-free (Mini-Hes) optimization for building an LFA model. Experiment results indicate that, with Mini-Hes, the LFA model outperforms several state-of-the-art models in addressing missing data estimation task.
  arXiv  Detail & Related papers  (2024-02-19T08:43:00Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR'16 [0.29998889086656577]
  We show that relatively minor modifications on a benchmark dataset cause significantly more impact on model performance than the specific ML technique considered. We also show that the measured model performance is uncertain, as a result of labelling inaccuracies.
  arXiv  Detail & Related papers  (2023-05-31T12:03:12Z)
• Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions [0.0]
  This paper examines two different yet related questions related to explainable AI (XAI) practices. The study compares various machine learning models, including single classifiers (logistic regression, decision trees, LDA, QDA), heterogeneous ensembles (AdaBoost, Random Forest), and sequential neural networks. Two advanced post-hoc model explainability techniques - LIME and SHAP are utilized to assess ML-based credit scoring models.
  arXiv  Detail & Related papers  (2022-09-19T21:44:42Z)
• How robust are pre-trained models to distribution shift? [82.08946007821184]
  We show how spurious correlations affect the performance of popular self-supervised learning (SSL) and auto-encoder based models (AE) We develop a novel evaluation scheme with the linear head trained on out-of-distribution (OOD) data, to isolate the performance of the pre-trained models from a potential bias of the linear head used for evaluation.
  arXiv  Detail & Related papers  (2022-06-17T16:18:28Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data [2.0824228840987447]
  This paper explores the use of non-linear interpretable machine learning (ML) models in classification problems. Various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. In one of the two real-world datasets, knowledge distillation method achieves improved AUC scores.
  arXiv  Detail & Related papers  (2022-04-04T17:56:37Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.