For the opinion analysis task on traditional Chinese texts at NTCIR-7, supervised approaches and ensemble techniques have been used and compared in our participating system. Two kinds of supervised approaches were employed here: 1) the supervised lexicon-based approach, and 2) machine learning approaches. Ensemble techniques were also used to combine the results given by different approaches. B...

Fuzzy cognitive maps have gained considerable research interest and widely used to analyze complex systems and making decisions. Recently they have been found large applicability in diverse domains for decision support and classification tasks. A new learning paradigm for FCMs is proposed in this research work, inheriting the main aspects of ensemble based learning approaches, such as bagging a...

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detect...

Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any or peptide is related to its structure sequence amino acids that make up it. There 20 types in humans, each them has a particular characteristic according chemical structure. Current machine deep learning models have been used classify ACPs problems. How...

The popularity of applying machine learning methods to computational linguistics problems has produced a large supply of trainable natural language processing systems. Most problems of interest have an array of off-the-shelf products or downloadable code implementing solutions using various techniques. Where these solutions are developed independently, it is observed that their errors tend to b...

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases:...

The advantage of ensemble methods over single methods is their ability to correct the errors of individual ensemble members and thereby improve the overall ensemble performance. This paper explores the relation between ensemble diversity and noise detection performance in the context of ensemble-based class noise detection by studying different diversity measures on a range of heterogeneous noi...

This paper presents a method for improved ensemble learning, by treating the optimization of an ensemble of classifiers as a compressed sensing problem. Ensemble learning methods improve the performance of a learned predictor by integrating a weighted combination of multiple predictive models. Ideally, the number of models needed in the ensemble should be minimized, while optimizing the weights...