@inproceedings { , title = {Simultaneous meta-data and meta-classifier selection in multiple classifier system.}, abstract = {In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier’s prediction thereby introducing a simultaneous meta-data and meta-classifier selection method for ensemble systems. Our approach uses Cross Validation on the training set to generate meta-data as the predictions of base classifiers. We then use Ant Colony Optimization to search for the optimal subset of meta-data and meta-classifier for the data. By considering each column of meta-data, we construct the configuration including a subset of these columns and a meta-classifier. Specifically, the columns are selected according to their corresponding pheromones, and the meta-classifier is chosen at random. The classification accuracy of each configuration is computed based on Cross Validation on meta-data. Experiments on UCI datasets show the advantage of proposed method compared to several classifier and feature selection methods for ensemble systems.}, conference = {2019 Genetic and evolutionary computation conference (GECCO '19)}, doi = {10.1145/3321707.3321770}, isbn = {9781450361118}, note = {INFO COMPLETE (checked 18/7/2019) PERMISSION GRANTED (version = AAM; embargo = none; licence = BY-NC; SHERPA= http://sherpa.ac.uk/romeo/pub/21/ ) DOCUMENT RECEIVED (AAM rec'd from contact 18/7/2019 LM)}, pages = {39-46}, publicationstatus = {Published}, publisher = {Association for Computing Machinery (ACM)}, url = {https://rgu-repository.worktribe.com/output/322649}, keyword = {Ensemble method, Multiple classifiers, Classifier fusion, Combining classifiers, Ensemble selection, Classifier selection, Feature selection, Ant colony optimization}, year = {2019}, author = {Nguyen, Tien Thanh and Luong, Anh Vu and Nguyen, Thi Minh Van and Ha, Trong Sy and Liew, Alan Wee-Chung and McCall, John} editor = {López-Ibáñez, Manuel} }