A decade ago, rapid prototyping (RP) machine selection process was much easier since the breadth of choice was smaller, and the strengths of each technology were distinct and readily apparent. With advances in established technologies, materials and the introduction of new methods, selecting the right RP machine has become much more difficult. These advances have blurred the lines of distinctio...

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...

This paper presents an application of supervised machine learning approaches to the classification of the colon cancer gene expression data. Established feature selection techniques based on principal component analysis (PCA), independent component analysis (ICA), genetic algorithm (GA) and support vector machine (SVM) are, for the first time, applied to this data set to support learning and cl...

The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the ...

Pre-processing is an important part of machine learning, and has been shown to significantly improve the performance of classifiers. In this paper, we take a selection of pre-processing methods—focusing specifically on discretization and feature selection—and empirically examine their combined effect on classifier performance. In our experiments, we take 11 standard datasets and a selection of ...

This paper presents an application of supervised machine learning approaches to the classification of the colon cancer gene expression data. Established feature selection techniques based on principal component analysis (PCA), independent component analysis (ICA), genetic algorithm (GA) and support vector machine (SVM) are, for the first time, applied to this data set to support learning and cl...

Machine involvement has the potential to speed up language documentation. We assess this potential with timed annotation experiments that consider annotator expertise, example selection methods, and suggestions from a machine classifier. We find that better example selection and label suggestions improve efficiency, but effectiveness depends strongly on annotator expertise. Our expert performed...

In this paper we present two very popular aspects in supervised Machine Learning algorithms: feature selection and active learning paradigm. Feature selection algorithms are widely used in Machine Learning to reduce the feature space representing given data samples. Active learning is very popular supervised Machine Learning technique that has been effectively used in Text Classification tasks ...