Trading Accuracy for Simplicity in Decision Trees
Machine Learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient algorithms for constructing decision trees with constraints
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive modeling in automotive direct marketing: tools, experiences and open issues
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Measuring lift quality in database marketing
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Post-pruning in decision tree induction using multiple performance measures
Computers and Operations Research
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Post-pruning in regression tree induction: An integrated approach
Expert Systems with Applications: An International Journal
Vote prediction by iterative domain knowledge and attribute elimination
International Journal of Business Intelligence and Data Mining
Information Technology and Management
Environmental Modelling & Software
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
The Knowledge Engineering Review
Pareto-optimality of oblique decision trees from evolutionary algorithms
Journal of Global Optimization
Expert Systems with Applications: An International Journal
Feature selection for paintings classification by optimal tree pruning
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Credit card churn forecasting by logistic regression and decision tree
Expert Systems with Applications: An International Journal
An iterative approach to build relevant ontology-aware data-driven models
Information Sciences: an International Journal
Computers and Electrical Engineering
Decision trees: a recent overview
Artificial Intelligence Review
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Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classification of future events. The induction of the decision tree is done using a supervised knowledge discovery process in which prior knowledge regarding classes in the database is used to guide the discovery. The generation of a DT is a relatively easy task but in order to select the most appropriate DT it is necessary for the DM project team to generate and analyze a significant number of DTs based on multiple performance measures. We propose a multi-criteria decision analysis based process that would empower DM project teams to do thorough experimentation and analysis without being overwhelmed by the task of analyzing a significant number of DTs would offer a positive contribution to the DM process. We also offer some new approaches for measuring some of the performance criteria.