Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of classifiers is to decide upon a way of combining the prediction of its base classifiers. In this paper, we introduce a novel grading-based algorithm for model combination, which uses cost-sensitive learning in building a meta-learn...

Ensemble learning techniques and in particular Random Forests have been one of the most successful machine learning approaches of the last decade. Despite their success, there exist barely suitable visualizations of Random Forests, which allow a fast and accurate understanding of how well they perform a certain task and what leads to this performance. This paper proposes an exemplar-driven visu...

Learning from streaming data represents an important and challenging task. Maintaining an accurate model, while the stream goes by, requires a smart way for tracking data changes through time, originating concept drift. One way to treat this kind of problem is to resort to ensemble-based techniques. In this context, the advent of new technologies related to web and ubiquitous services call for ...

We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, sparse observations space time. propose a novel methodology that harnesses transportation of measures, convex optimization, ideas from probabilistic graphical to yield robust ensemble approximations the distribution high dimensions. Our approach...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...

Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each ...

The problem of combining predictors to increase accuracy (often called ensemble learning) has been studied broadly in the machine learning community for both classification and regression tasks. The design of an ensemble is based on the individual accuracy of the predictors and also how different they are from one another. There is a significant body of literature on how to design and measure d...

Classification is one of the critical task in datamining. Many classifiers exist for classification task and each have their own pros and cons. It is observed that due to imbalancing in datasets quality of classification accuracy is decreasing. Thus the increasing rate of data diversity and size decreases the performance and efficiency of classifiers. Thus it is very much important to get the m...

In this paper, a robust online sequential extreme learning machine (ROS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm opt imization selective ensemble (PSOSEN) is proposed. Noting that PSOSEN is a general selective ensembl...

We introduce a domain-specific and late-fusion algorithm to cope with the challenge raised in The MediaEval 2017 Multimedia Satellite Task. Several known techniques are integrated based on domainspecific criteria such as late fusion, tuning, ensemble learning, object detection using deep learning, and temporal-spatial-based event confirmation. Experimental results show that the proposed algorit...