Feature selection based data mining methods is one of the most important research directions in the fields of machine learning in recent years. This paper presents a review of assorted feature selection methods named filter, wrapper and embedded and multiclass classifiers like support vector machines (SVM), decision tree, averaged perceptron and neural network. Additionally it conveys an assess...

In the future, biomass will continue to emerge as a viable source of chemicals. The development of new industries that utilize bio-renewables provides opportunities for innovation. For example, bio- and chemo-catalysts can be combined in 'one pot' to prepare chemicals of commercial value. This has been demonstrated using isolated enzymes and whole cells for a variety of chemical transformations...

Radiolabeled monoclonal antibodies, have created new innovations in diagnosis, research and therapy of disease in last 2 decades. One of the serious limitation of applications of radiolabeled antibodies in vivo is relatively low target to background activity. Various strategies have been proposed to solve this problem including pre-targeting methods that was suggested in 1989. Regarding i...

This paper presents a new active learning paradigm which considers not only the uncertainty of the classifier but also the diversity of the corpus. The two measures for uncertainty and diversity were combined using the MMR (Maximal Marginal Relevance) method to give the sampling scores in our active learning strategy. We incorporated MMR-based active machinelearning idea into the biomedical nam...