Comprehensible credit scoring models using rule extraction from support vector machines
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Summary
This paper provides an overview of the recently proposed rule extraction techniques for SVMs and introduces two others taken from the artificial neural networks domain, being Trepan and G-REX, which rank at the top of comprehensible classification techniques.
- Type
- article
- Published
- 2007-12-16
- Cited by
- 476
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W1970959042
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17731008
Keywords
Support vector machine, Computer science, Artificial intelligence, Machine learning, Extraction (chemistry)
References
- Neural Networks and Related Methods for Classification
- Programs for Machine Learning
- Learning-based Rule-Extraction from Support Vector Machines
- Rule Extraction from Support Vector Machines
- The Truth is in There : Rule Extraction from Opaque Models Using Genetic Programming
- Benchmarking state-of-the-art classification algorithms for credit scoring
- An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring
- Biological data mining with neural networks: implementation and application of a flexible decision tree extraction algorithm to genomic problem domains
- Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation
- Survey and critique of techniques for extracting rules from trained artificial neural networks
- Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
- Rule extraction from linear support vector machines
- Bayesian kernel based classification for financial distress detection
- Financial time series prediction using least squares support vector machines within the evidence framework
- Extracting Tree-Structured Representations of Trained Networks
- The cost-minimizing inverse classification problem: a genetic algorithm approach
- C4.5: Programs for Machine Learning
- Extracting comprehensible models from trained neural networks
- Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction
- Benchmarking Least Squares Support Vector Machine Classifiers
Cited by
- Multi-Modal Scene Understanding for Robotic Grasping
- Credit Scoring Using Machine Learning
- A new method for predicting the outcome of speculative events
- Explaining Data-Driven Document Classifications
- Node classification over bipartite graphs through projection
- From possibilistic similarity measures to possibilistic decision trees.
- Profit-based feature selection using support vector machines - General framework and an application for customer retention
- Wybrane metody oceny i przycinania reguł decyzyjnych
- Individual-Based Modeling and Data Analysis of Ecological Systems Using Machine Learning Techniques
- Demonstrating non-inferiority of easy interpretable methods for insolvency prediction
- Unterstützung kundenbezogener Entscheidungsprobleme
- A New Approach for Discovering Business Process Models from Event Logs
- Combining B&B-based hybrid feature selection and the imbalance-oriented multiple-classifier ensemble for imbalanced credit risk assessment
- Credit Scoring Models Using Soft Computing Methods: A Survey
- Understanding Support Vector Machine Classifications via a Recommender System-Like Approach
- Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms - Support vector regression forecast combinations
- Bio-Inspired Credit Risk Analysis
- Detecting Domestic Violence - Showcasing a Knowledge Browser based on Formal Concept Analysis and Emergent Self Organizing Maps
- Enhancing genetic programming for predictive modeling
- Un modelo neuronal basado en la metaplasticidad para la clasificación de objetos en señales 1-d y 2-d
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