Reservoir gate opening classification using multiple classifier system with ant system-based feature decomposition

Classification of reservoir gate opening (RGO) is an important task in flood management.Reservoir water level has been used to determine the number of gates to be opened when flood is imminent to prevent disaster.Predicting the number of gates to be opened is crucial to avoid any disaster. Multiple classifier system has been shown to provide better classification accuracy as compared to single classifier system.However, there is no guideline on the number of classifiers to be combined and no measurement was proposed to measure the compactness of the classifiers.This study proposes an ant system-based feature decomposition approach to develop a multiple classifier ensemble for classification of RGO.Experiments have been conducted using the k-nearest neighbour, decision tree, nearest mean classifier and linear discriminant analysis as base classifier, and performance of ant system has been compared with random subspace method.Based on the results, it can be concluded that the multiple classifier with ant system-based feature decomposition produced better classification accuracy than random subspace method. Best classification results were obtained when multiple decision tree is constructed to make predictions of RGO with an average accuracy of 89.17%. This method is expected to be useful to apply for RGO classification and future work can be done to include rainfall precipitation besides reservoir water level

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performanc

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe

Proceedings of the 4th Workshop of the MPM4CPS COST Action

Proceedings of the 4th Workshop of the MPM4CPS COST Action with the presentations delivered during the workshop and papers with extended versions of some of them

Designing multiple classifier combinations a survey

Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination which are determining the classifier ensemble and combiner construction. This paper reviews approaches in constructing the classifier ensemble and combiner. For each approach, methods have been reviewed and their advantages and disadvantages have been highlighted. A random strategy and majority voting are the most commonly used to construct the ensemble and combiner, respectively. The results presented in this review are expected to be a road map in designing multiple classifier combinations