This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene E...

Ensembles of classifiers represent one of the main research directions in machine learning. Two main theories are invoked to explain the success of ensemble methods. The first one consider the ensembles in the framework of large margin classifiers, showing that ensembles enlarge the margins, enhancing the generalization capabilities of learning algorithms. The second is based on the classical b...

Most of the well-known ensemble techniques use the same training algorithm and the same sequence of patterns from the learning set to adapt the trainable parameters (weights) of the neural networks in the ensemble. In this paper, we propose to replace the traditional training algorithm in which the sequence of patterns is kept unchanged during learning. With the new algorithms we want to add di...

A common step in drug design is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure of a compound relates to its biological activity. There is growing interest in the application of machine learning techniques in QSAR modeling research. However, no single technique can claim to be uniformly supe...

Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research. The lack of robust and universally applicable cluster validity scores often makes the algorithm selection and hyperparameter evaluation a tough guess. In this paper, we show that cluster ensemble aggregation techniques such as conse...

Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-offorests model, a generalization of the ensembleof-trees model of Meilă and Jaakkola (2006). Our model enables structure learning of Markov random fields (MRF) with multiple connected components and arbitrary p...

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across sub-populations— heterogeneous treatment effects —and how differences in the content of the treatment affects responses—the response to heterogeneous tr...

In this paper, we propose a method that evaluates the content of a text summary using a machine learning approach. This method operates by combining multiple features to build models that predict the PYRAMID scores for new summaries. We have tested several single and ”Ensemble Learning” classifiers to build the best model. The evaluation of summarization system is made using the average of the ...

This study investigates the classification performances of emotion and autism spectrum disorders from speech utterances using ensemble classification techniques. We first explore the performances of three well-known machine learning techniques, namely, support vector machines (SVM), deep neural networks (DNN) and k-nearest neighbours (KNN), with acoustic features extracted by the openSMILE feat...

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentati...