International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Data mining for process and quality control in the semiconductor industry
Data mining for design and manufacturing
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Linear Models and Generalizations: Least Squares and Alternatives
Linear Models and Generalizations: Least Squares and Alternatives
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A hybrid sales forecasting system based on clustering and decision trees
Decision Support Systems
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
A parameterless feature ranking algorithm based on MI
Neurocomputing
A novel Supervised Instance Selection algorithm
International Journal of Business Intelligence and Data Mining
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
GEP-Induced Expression Trees as Weak Classifiers
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Improving data mining utility with projective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5
Expert Systems with Applications: An International Journal
A novel feature selection approach by hybrid genetic algorithm
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A fuzzy ARTMAP model with contraction procedure
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Automated detection of injected faults in a differential equation solver
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Predictive maintenance with multi-target classification models
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
A new evolutionary neural network classifier
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Abstract--We describe and evaluate an information-theoretic algorithm for data-driven induction of classification models based on a minimal subset of available features. The relationship between input (predictive) features and the target (classification) attribute is modeled by a tree-like structure termed an information network (IN). Unlike other decision-tree models, the information network uses the same input attribute across the nodes of a given layer (level). The input attributes are selected incrementally by the algorithm to maximize a global decrease in the conditional entropy of the target attribute. We are using the prepruning approach: When no attribute causes a statistically significant decrease in the entropy, the network construction is stopped. The algorithm is shown empirically to produce much more compact models than other methods of decision-tree learning while preserving nearly the same level of classification accuracy.