Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Modern heuristic techniques for combinatorial problems
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Feature selection in scientific applications
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Evolving rule-based systems in two medical domains using genetic programming
Artificial Intelligence in Medicine
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Detecting novel hypermethylated genes in Breast cancer benefiting from feature selection
Computers in Biology and Medicine
Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis
Computers in Biology and Medicine
Unsupervised segmentation and classification of cervical cell images
Pattern Recognition
Expert Systems with Applications: An International Journal
Cervical cell classification based exclusively on nucleus features
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
DBNs-BLR (MCMC) -GAs-KNN: a novel framework of hybrid system for thalassemia expert system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in different times by expert MDs, consisting of 917 and 500 images of pap smear cells, respectively. Each cell is described by 20 numerical features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem. For finding the best possible performing feature subset selection problem, an effective genetic algorithm scheme is proposed. This algorithmic scheme is combined with a number of nearest neighbor based classifiers. Results show that classification accuracy generally outperforms other previously applied intelligent approaches.