Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Hybrid algorithms with instance-based classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
MML inference of oblique decision trees
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Constructing accurate fuzzy classifiers: A new adaptive method for rule-weight specification
International Journal of Knowledge-based and Intelligent Engineering Systems
An interactive co-evolutionary CAD system for garment pattern design
Computer-Aided Design
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier
Expert Systems with Applications: An International Journal
Short communication: A new intelligent hepatitis diagnosis system: PCA-LSSVM
Expert Systems with Applications: An International Journal
A New Expert System for Diagnosis of Lung Cancer: GDA--LS_SVM
Journal of Medical Systems
A hybrid expert system approach for telemonitoring of vocal fold pathology
Applied Soft Computing
Hi-index | 12.06 |
Lung cancers are cancers that begin in the lungs. Other types of cancers may spread to the lungs from other organs. However, these are not lung cancers because they did not start in the lungs. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lung cancer using principles component analysis (PCA), fuzzy weighting pre-processing and artificial immune recognition system (AIRS). The approach system has three stages. First, dimension of lung cancer dataset that has 57 features is reduced to four features using principles component analysis. Second, a new weighting scheme based on fuzzy weighting pre-processing was utilized as a pre-processing step before the main classifier. Third, artificial immune recognition system was our used classifier. We took the lung cancer dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.