Neuro-Ensemble for Time Series Data Classification

Combining a set of classification algorithms is a powerful technique in improving the accuracy of individual classifiers. There are two main paradigms in combining classifiers: classifier selection, where each classifier is considered as an expert in some local area of the feature space, and classifier fusion, where all classifiers are trained over the entire feature space and they are considered as competitive and complementary to each other. In this paper, we propose a new ensemble technique, NeuroEnsemble, that follows the classifier fusion paradigm applied on time series data. The Neuro-Ensemble exploits the idea that different classifiers participating in the ensemble have varying degrees of expertise on learning different class labels and it optimizes the ensemble using a shallow Multi-Layer Perceptron (MLP) based meta-learner to capture the expertise of individual classifiers. Every neuron in the MLP represents a classifier that contributes with a vote and performs activation and state computations. This work is the first attempt to train a neural network for learning the expertise of each classifier in an ensemble and optimize the entire classification schema based on class-level expertise weights. We validated our Neuro-Ensemble on 43 real-world time series datasets from the UCR repository. Our experimental results show the effectiveness and efficiency of our approach in comparison with individual baseline learners and ensemble techniques.