C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Adaptive estimated maximum-entropy distribution model
Information Sciences: an International Journal
Privacy-preserving multi-party decision tree induction
International Journal of Business Intelligence and Data Mining
International Journal of Business Intelligence and Data Mining
RFID-based human behavior modeling and anomaly detection for elderly care
Mobile Information Systems
RFID-based human behavior modeling and anomaly detection for elderly care
Mobile Information Systems
A New Similarity Metric for Sequential Data
International Journal of Data Warehousing and Mining
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Classification is a fundamental problem in machine learning and data mining. This paper applies a stochastic optimization model to classification problems. The proposed maximum entropy estimated distribution model uses a probabilistic distribution to represent solution space, and a sampling technique to explore search space. This paper demonstrates the application of the proposed maximum entropy estimated distribution model to improve linear discriminant function and rule induction methods. In addition, this paper compares the proposed classification model with decision trees. It shows that the proposed model is preferable to decision tree C4.5 in the following cases: i) when prior distribution of classification is available; ii) when no assumption is made about underlying classification structure; and iii) when a classification problem is multimodal in nature.