pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. the surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (seldi-tof ms) has been used to generate proteomic profiles from biological fluids. mass spectrometry yields redundant noisy data that the most data points are irrelevant features ...

The eeectiveness of classiier-independent feature selection is described. The aim is to remove garbage features and to improve the classiication accuracy of all the practical classiiers compared with the situation where all the given features are used. Two algorithms of classiier-independent feature selection and two other conventional classiier-speciic algorithms are compared on three sets of ...

Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results....

Abstract We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated the data, e.g., online bin-packing. Specifically we train two types of recurrent neural networks predict packing heuristic bin-packing, selecting from four well-known heuristics. As input, RNN methods only use sequence item-sizes. This ...

Abstract Sample correlations and feature relations are two pieces of information that needed to be considered in the unsupervised selection, as labels missing guide model construction. Thus, we design a novel selection scheme, this paper, via considering completed sample dependencies unified framework. Specifically, self-representation graph construction conducted preserve select important neig...

This paper investigates the use of fMRI data to develop a classifier to identify a subject’s cognitive state during a particular time interval. In particular, data from a set of subjects is used to decode the cognitive state of a new subject not used in the training process. This is a difficult task because each subject may produce different activation for a particular task and each has a diffe...

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...