Machine Learning
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Medical Imaging and Informatics
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Breast-Cancer identification using HMM-fuzzy approach
Computers in Biology and Medicine
Hybrid mammogram classification using rough set and fuzzy classifier
Journal of Biomedical Imaging
Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing
Expert Systems with Applications: An International Journal
Designing an artificial immune system-based machine learning classifier for medical diagnosis
ICICA'10 Proceedings of the First international conference on Information computing and applications
Expert Systems with Applications: An International Journal
Design Ensemble Machine Learning Model for Breast Cancer Diagnosis
Journal of Medical Systems
Computers in Biology and Medicine
Extended fuzzy c-means: an analyzing data clustering problems
Cluster Computing
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
A neuro-fuzzy immune inspired classifier for task-oriented texts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.