Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Inductive modelling in law: example based expert systems in administrative law
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
A comparison of the decision table and tree
Communications of the ACM
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning - Special issue on learning with probabilistic representations
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Prior Knowledge in Economic Applications of Data Mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Learning from Inconsistent and Noisy Data: The AQ18 Approach
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
From Ensemble Methods to Comprehensible Models
DS '02 Proceedings of the 5th International Conference on Discovery Science
Learning with Globally Predictive Tests
DS '98 Proceedings of the First International Conference on Discovery Science
Extracting decision trees from trained neural networks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Cooperative Game Theoretic Approach to Prototype Selection
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Nearest Neighbour Classification with Monotonicity Constraints
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
Adding monotonicity to learning algorithms may impair their accuracy
Expert Systems with Applications: An International Journal
A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
An experimental test of Occam's razor in classification
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
Performance of classification models from a user perspective
Decision Support Systems
Data mining for indicators of early mortality in a database of clinical records
Artificial Intelligence in Medicine
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Artificial Intelligence in Medicine
Improving the interpretability of classification rules discovered by an ant colony algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The vast majority of the literature evaluates the performance of classification models using only the criterion of predictive accuracy. This paper reviews the case for considering also the comprehensibility (interpretability) of classification models, and discusses the interpretability of five types of classification models, namely decision trees, classification rules, decision tables, nearest neighbors and Bayesian network classifiers. We discuss both interpretability issues which are specific to each of those model types and more generic interpretability issues, namely the drawbacks of using model size as the only criterion to evaluate the comprehensibility of a model, and the use of monotonicity constraints to improve the comprehensibility and acceptance of classification models by users.