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Many real-world machine learning tasks have very limited labeled data but a large amount of unlabeled data. To take advantage of the unlabeled data for enhancing learning performance, several semi-supervised learning techniques have been developed. In this paper, we propose a novel semi-supervised ensemble learning algorithm, termed Multi-Train, which generates a number of heterogeneous classifiers that use different classification models and/or different features. During the training process, each classifier is refined using unlabeled data, which are labeled by the majority prediction of the rest classifiers. We hypothesize that the use of different models and different input features can promote the diversity of the ensemble, thereby improving the performance compared to existing methods such as the co-training and tri-training algorithms. Experimental results on the UCI datasets clearly demonstrated the effectiveness of using heterogeneous ensembles in semi-supervised learning. [ABSTRACT FROM AUTHOR]

This paper presents a competitive co-evolutionary (ComCoE) that engages a double elimination tournament (DET) to evolve artificial neural networks (ANNs) for undertaking data classification problems. The proposed model performs a global search by a ComCoE approach to find near optimal solutions. During the global search process, two populations of different ANNs compete and fitness evaluation of each ANN is made in a subjective manner based on their participations throughout a DET which promotes competitive interactions among individual ANNs. The adaptation and fitness evaluation processes drive the global search for a more competent ANN classifier. A winning ANN is identified from the global search. Then, the Scaled Conjugate Backpropagation algorithm, which is a local search, is performed to further train the winning ANN to obtain a precise solution. The performance of the proposed classification model is evaluated rigorously; its performance is compared with the baseline ANNs of the proposed model as well as other classifiers. The results indicate that the proposed model could construct an ANN which could produce high classification accuracy rates with a compact network structure. [ABSTRACT FROM AUTHOR]

A new algorithm for ensemble construction based on adapted restricting a set of weights of examples in training data to avoid overfitting and to reduce a number of iterations is proposed in the paper. The algorithm called IDMBoost (Imprecise Dirichlet Model Boost) applies Walley׳s imprecise Dirichlet model for modifying the restricted sets of weights depending on the number and location of classification errors. Updating of weights within the restricted set (simplex) is carried out by using its extreme points. The proposed algorithm has a double adaptation procedure. The first adaptation is carried out within every restricted simplex like the AdaBoost. The second adaptation reduces and changes the restricted sets of possible weights of examples. Various numerical experiments with real data illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speeded up of up to 3.461×when compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptable. [Copyright &y& Elsevier]

Abstract: This paper introduces Sphere Support Vector Machines (SVMs) as the new fast classification algorithm based on combining a minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three significantly speeds up the training phase of SVMs and also attains practically the same accuracy as the other classification models over several large real datasets within the strict validation frame of a double (nested) cross-validation. The results shown are promoting SphereSVM as outstanding alternatives for handling large and ultra-large datasets in a reasonable time without switching to various parallelization schemes for SVM algorithms recently proposed. [Copyright &y& Elsevier]