A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Shape quantization and recognition with randomized trees
Neural Computation
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Robust algorithms for principal component analysis
Pattern Recognition Letters
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
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Machine Learning
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Feature subset selection in large dimensionality domains
Pattern Recognition
On the classification performance of TAN and general Bayesian networks
Knowledge-Based Systems
Data & Knowledge Engineering
Pattern Recognition Letters
A GAs based approach for mining breast cancer pattern
Expert Systems with Applications: An International Journal
A genetic algorithm-based rule extraction system
Applied Soft Computing
Information Sciences: an International Journal
Bagging schemes on the presence of class noise in classification
Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
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Artificial Intelligence in Medicine
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Artificial Intelligence in Medicine
Wrapper feature selection for small sample size data driven by complete error estimates
Computer Methods and Programs in Biomedicine
Differential Evolution for automatic rule extraction from medical databases
Applied Soft Computing
Computer-aided diagnosis system: A Bayesian hybrid classification method
Computer Methods and Programs in Biomedicine
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Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA.