International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction games and arcing algorithms
Neural Computation
Evolving Multilayer Perceptrons
Neural Processing Letters
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Cross-Validated C4.5: Using Error Estimation for Automatic Parameter Selection
Cross-Validated C4.5: Using Error Estimation for Automatic Parameter Selection
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Neural Networks - 2005 Special issue: IJCNN 2005
Non-parametric classifier-independent feature selection
Pattern Recognition
Ensemblator: An ensemble of classifiers for reliable classification of biological data
Pattern Recognition Letters
Ex-ray: Data mining and mental health
Applied Soft Computing
A co-evolving decision tree classification method
Expert Systems with Applications: An International Journal
Classification tree analysis using TARGET
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
Expert Systems with Applications: An International Journal
Knowledge and Information Systems
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble
IEEE Transactions on Information Technology in Biomedicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Class proximity measures - Dissimilarity-based classification and display of high-dimensional data
Journal of Biomedical Informatics
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic Computing
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
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Data-mining algorithms have been used in many classification problems. Among them, the decision tree (DT), back-propagation network (BPN), and support vector machine (SVM) are popular and can be applied to various areas. Nevertheless, different problems may require different parameter values when applying DT, BPN or SVM. If parameter values are not set well, results may turn out to be unsatisfactory. Further, a dataset may contain many features; however, not all features are beneficial for classifications. Therefore, a scatter search (SS) approach is proposed to obtain the better parameters and select the beneficial subset of features to attain better classification results. The above classification algorithms have their respective advantages and disadvantages, and suitability is influenced by the characteristics of the problem. If the algorithms can function together in a so-called ensemble, it is expected that better results can be obtained. Therefore, this study adapts ensemble to further enhance the classification accuracy rate. In order to evaluate the performance of the proposed approach, datasets in UCI (University of California, Irvine) were applied as the test problem set. The corresponding results were compared to several well-known, published approaches. The comparative study shows that the proposed approach improved the classification accuracy rate in most datasets. Thus, the proposed approach can be useful to both practitioners and researchers.