C4.5: programs for machine learning
C4.5: programs for machine learning
The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Knowledge Discovery in Databases
Knowledge Discovery in Databases
An Imunogenetic Technique To Detect Anomalies In Network Traffic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Granular computing
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computer aided diagnosis of ECG data on the least square support vector machine
Digital Signal Processing
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
Speed boosting induction of fuzzy rules with artificial immune systems
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Mining fuzzy classification rules using an artificial immune system with boosting
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
| Hi-index | 0.00 |
Classification is an important data mining task in biomedicine. For easy comprehensibility, rules are preferrable to another functions in the analysis of biomedical data. The aim of this work is to use a new fuzzy immune rule-based classification system for a medical diagnosis of a cardiovascular disease. In this study, fuzzy immune approach (FIA), which can be improved by ours, is a new method and firstly, it is applied to ECG dataset. The performance of the proposed approach, in terms of classification accuracy, ROC curves, and area under the ROC curve (AUC) was compared with traditional classifier schemes: C4.5, Naïve Bayes, KStar, Meta END, and ANN. The classification accuracies and AUC statistics of FIA for the data sets used are the highest among the classifiers reported on the UCI website and other classifiers used for related problems and tested by cross validation.