Data mining and classification is a growing and important field in bioinformatics. Machine learning algorithms such as support vector machines can be used with genetic information to predict disease susceptibility. In particular, single nucleotide polymorphisms have been analyzed to classify an individual into "sick" or "healthy" categories for a specific genetic disorder. The most computationa...

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new ap...

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...

although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. but, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. this paper considers the problem of scheduling no-idle hybrid flow shops. a mixed integer linear programming model is first developed to mathematically form...

This review examines the methods used to optimize process parameters of laser cladding, including traditional optimization algorithms such as single-factor, regression analysis, response surface, and Taguchi, well intelligent system neural network models, genetic algorithms, support vector machines, new non-dominance ranking algorithm II, particle swarm algorithms. The advantages disadvantages ...

We propose an algorithm for exploring the entire regularization path of asymmetric-cost linear support vector machines. Empirical evidence suggests the predictive power of support vector machines depends on the regularization parameters of the training algorithms. The algorithms exploring the entire regularization paths have been proposed for single-cost support vector machines thereby providin...

In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators...

Text classification is one of the areas where machine learning algorithms are used. The size dataset and methods used for converting textual words into vectors play a major role in classifying them. This paper proposes heuristic based approach to classify documents using Genetic Algorithm aided Support Vector Machines (SVM) Ensemble Learning approach. real valued representation data done on app...