Name
Title | ||
|---|---|---|
Combining Multi Classifiers Based On A Genetic Algorithm - A Gaussian Mixture Model Framework |
Abstract | ||
|---|---|---|
Combining outputs from different classifiers to achieve high accuracy in classification task is one of the most active research areas in ensemble method. Although many state-of-art approaches have been introduced, no one method performs the best on all data sources. With the aim of introducing an effective classification model, we propose a Gaussian Mixture Model (GMM) based method that combines outputs of base classifiers (called meta-data or Level1 data) resulted from Stacking Algorithm. We further apply Genetic Algorithm (GA) to that data as a feature selection strategy to explore an optimal subset of Level1 data in which our GMM-based approach can achieve high accuracy. Experiments on 21 UCI Machine Learning Repository data files and CLEF2009 medical image database demonstrate the advantage of our framework compared with other well-known combining algorithms such as Decision Template, Multiple Response Linear Regression (MLR), SCANN and fixed combining rules as well as GMM-based approaches on original data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-09339-0_6 | INTELLIGENT COMPUTING METHODOLOGIES |
Keywords | Field | DocType |
Stacking Algorithm, feature selection, Gaussian Mixture Model, Genetic Algorithm, multi-classifier system, classifier fusion, combining classifiers, ensemble method | Data mining,Feature selection,Computer science,Combining rules,Artificial intelligence,Image database,Data file,Genetic algorithm,Linear regression,Pattern recognition,Expert system,Mixture model,Machine learning | Conference |
Volume | ISSN | Citations |
8589 | 0302-9743 | 7 |
PageRank | References | Authors |
0.44 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tien Thanh Nguyen | 1 | 79 | 12.55 |
| Alan Wee-Chung Liew | 2 | 799 | 61.54 |
| Minh Toan Tran | 3 | 20 | 1.81 |
| Mai Phuong Nguyen | 4 | 46 | 3.82 |