Machine Learning
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
Specializing for predicting obesity and its co-morbidities
Journal of Biomedical Informatics
Vibration based fault diagnosis of monoblock centrifugal pump using decision tree
Expert Systems with Applications: An International Journal
Application of decision tree based on C4.5 in analysis of coal logistics customer
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Improved C4.5 algorithm for rule based classification
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Input space reduction for rule based classification
WSEAS Transactions on Information Science and Applications
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Expert Systems with Applications: An International Journal
Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump
International Journal of Data Analysis Techniques and Strategies
Expert Systems with Applications: An International Journal
A prototype classifier based on gravitational search algorithm
Applied Soft Computing
WSEAS Transactions on Information Science and Applications
Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases
HIS'12 Proceedings of the First international conference on Health Information Science
Predicting seminal quality with artificial intelligence methods
Expert Systems with Applications: An International Journal
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
Expert Systems with Applications: An International Journal
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
An empirical study on the use of mutant traces for diagnosis of faults in deployed systems
Journal of Systems and Software
Hi-index | 12.06 |
Generally, many classifier systems compel in the classification of multi-class problems. The aim of this study is to improve the classification accuracy in the case of multi-class classification problems. In this study, we have proposed a novel hybrid classification system based on C4.5 decision tree classifier and one-against-all approach to classify the multi-class problems including dermatology, image segmentation, and lymphography datasets taken from UCI (University of California Irvine) machine learning database. To test the proposed method, we have used the classification accuracy, sensitivity-specificity analysis, and 10-fold cross validation. In this work, firstly C4.5 decision tree has been run for all the classes of dataset used and achieved 84.48%, 88.79%, and 80.11% classification accuracies for dermatology, image segmentation, and lymphography datasets using 10-fold cross validation, respectively. The proposed method based on C4.5 decision tree classifier and one-against-all approach obtained 96.71%, 95.18%, and 87.95% for above datasets, respectively. These results show that the proposed method has produced very promising results in the classification of multi-class problems. This method can be used in many pattern recognition applications. In future, instead of C4.5 decision tree, other classification algorithms such as Bayesian learning, artificial immune system algorithms, artificial neural networks can be used.