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...

In this paper a planning framework based on Ant Colony Optimization techniques is presented. It is well known that finding optimal solutions to planning problems is a very hard computational problem. Stochastic methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several...

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...

— In this paper, we propose a powerful and convenient querying model, "Queryball", for users to query in immersive VR systems. In immersive VR systems, users expect intuitive and heuristic interactive operations derived from empirical knowledge in the real world. As a possible method for the interactive operations, interaction by virtual tools has been proposed. However, intuitive and heuristic...

A relevance filter is proposed which removes features based on the mutual information between class labels and features. It is proven that both feature independence and class conditional feature independence are required for the filter to be statistically optimal. This could be shown by establishing a relationship with the conditional relative entropy framework for feature selection. Removing f...

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 ...