In this paper two methods for selecting input features for a neural network used to aid iconic retrieval in an image database are presented and compared. The rst method involves training the network on all the feature inputs and then analysing the weight values in an attempt to nd the more important input features. The second borrows a method from statistical feature selection known as the sequ...

The analysis of the financial market always draws a lot of attention from investors and researchers. The trend of stock market is very complex and is influenced by various factors. Therefore to find out the most significant factors to the stock market is very important. Feature Selection is such an algorithm that can remove the redundant and irrelevant factors, and figure out the most significa...

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The proposed hybrid approaches provide the possibility of efficiently applying any subset ...

IEEE Binary Floating-Point is an industrystandard architecture. The IBM System/360 hexadecimal floating-point architecture predates the IEEE standard and has been carried forward through the System/370 to current System/390 processors. The growing importance of industry standards and floatingpoint combined to produce a need for IEEE Floating-Point on System/390. At the same time, customer inves...

In this paper we describe feature selection experiments for on-line handwriting recognition. We performed a sequential forward search through a 25 elements set of on-line and pseudo-off-line features. In our experiments we obtained interesting results. Using a set of only five features, we achieved a performance that is similar that of the reference system that uses all features. The best subse...

We propose a novel feature selection algorithm for liver tissue pathological image classification. To improve the efficiency of feature selection, the same feature values of positive and negative samples are removed in rough selection. To obtain the optimal feature subset, a new heuristic search algorithm, which is called Maximum Minimum Backward Selection (MMBS), is proposed in precise selecti...