Clustering is an important tool for data exploration. Several clustering algorithms exist, and new algorithms are frequently proposed in the literature. These algorithms have been very successful in a large number of real-world problems. However, there is no clustering algorithm, optimizing only a single criterion, able to reveal all types of structures (homogeneous or heterogeneous) present in...

Feature selection methods are essential to identify a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies showed the defectiveness in terms of specific biases of single feature selection methods, whereas an ensemble of feature selection techniques has the advantage to alleviate and com...

Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) ensemble, the number of classifiers needed i...

This paper presents an approach with ensemble classifiers using unsupervised data selection for speaker recognition. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. Based on its acoustic characteristics, the speech utterance is divided into several subsets using unsupervised data select...

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is important task because it can improve the planning and operation of photovoltaic systems, resulting economic advantages. Traditionally, single models are employed this task. However, issues regarding selection inappropriate model, misspecification, or presence random fluctuations solar series...

An efficient cluster head selection algorithm in wireless sensor networks is proposed in this paper. The implementation of the proposed algorithm can improve energy which allows the structured representation of a network topology. According to the residual energy, number of the neighbors, and the centrality of each node, the algorithm uses Fuzzy Inference Systems to select cluster head. The alg...