In this paper we introduce a novel approach for classifier and feature selection in a multi-classifier system using Genetic Algorithm (GA). Specifically, we propose a 2-part structure for each chromosome in which the first part is encoding for classifier and the second part is encoding for feature. Our structure is simple in the implementation of the crossover as well as the mutation stage of GA. We also study 8 different fitness functions for our GA algorithm to explore the optimal fitness functions for our model. Experiments are conducted on both 14 UCI Machine Learning Repository and CLEF2009 medical image database to demonstrate the benefit of our model on reducing classification error rate.
In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace.
In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e.
This paper introduces a novel 2-stage classification system with stacking and genetic algorithm (GA) based feature selection. Specifically, Lev-el1 data is first generated by stacking on the original data (called Level0 data) with base classifiers. Level1data is then classified by a second classifier (denoted by C) with feature selection using GA. The advantage of applying GA on Level1 data is that it has lower dimension and is more uniformity than Level0 data. We conduct experiments on both 18 UCI data files and CLEF2009 medical image database to demonstrate superior performance of our model in comparison with several popular combining algorithms.
This paper introduces a mechanism to learn optimal combining classifier algorithms associated with features and classifiers from an ensemble system. By using Genetic Algorithm approach that focuses on 3 objectives namely number of correct classified observations and number of selected features and classifiers, optimal solution can be achieved after several interactions of crossover and mutation. We also employ OWA operator in which a weight vector is built by Linear Decreasing (LD) function to average outputs from combining algorithms. Experiments on 2 well-known UCI Machine Learning Repository datasets demonstrate benefits of our approach compared with other state-of-art ensemble method like Decision Template and SCANN as well as all fixed combining algorithms in the ensemble system.
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