International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Improved training via incremental learning
Proceedings of the sixth international workshop on Machine learning
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
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
ROLEX-SP: Rules of lexical syntactic patterns for free text categorization
Knowledge-Based Systems
NB+: An improved Naïve Bayesian algorithm
Knowledge-Based Systems
A generalized cluster centroid based classifier for text categorization
Information Processing and Management: an International Journal
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This paper presents a novel algorithm named ID6NB for extending decision tree induced by Quinlan's non-incremental ID3 algorithm. The presented approach is aimed at suggesting the solutions for few unhandled exceptions of the Decision tree induction algorithms such as (i) the situation in which the majority voting makes incorrect decision (generating two different types of rules for same data), and (ii) in case of dimensionality reduction by decision tree induction algorithms, the determination of appropriate attribute at a node where two or more attributes have equal highest information gain. Exception due to majority voting is handled with the help of Naive Bayes algorithm and also novel solutions are given for dimensionality reduction. As a result, the classification accuracy has drastically improved. An extensive experimental evaluation on a number of real and synthetic databases shows that ID6NB is a state-of-the-art classification algorithm that outperforms well than other methods of decision tree learning.