Feature selection (FS) is a fundamental problem in the field of pattern recognition, which aims to find a minimal feature subset from the original feature space while retaining a suitably high accuracy in representing the original features. FS is used to improve the efficiency of learning algorithm especially for large scale datasets, by finding a minimal subset of features that has maximum eff...

The recent Chu et al. (2012) manuscript discusses two key findings regarding feature selection (FS): (1) data driven FS was no better than using whole brain voxel data and (2) a priori biological knowledge was effective to guide FS. Use of FS is highly relevant in neuroimaging-based machine learning, as the number of attributes can greatly exceed the number of exemplars. We strongly endorse the...

MUTUAL INFORMATION BASED FEATURE SELECTION FOR ACOUSTIC AUTISM DIAGNOSIS Pervasive Developmental Disorders (PDD) are known to affect children’s social interactions and mental development. Prosodic and linguistic cues can be used to diagnose the disorders at early ages. Computational paralinguistics can be applied for tele-monitoring and/or educating the children with PDD. For better understandi...

Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant features improve the accuracy of classification at same time. great challenge to balance conflict between two goals feature ratio. The evolutionary algorithm has been proved be suitable for selection. Recently, new meta-heuristic named crow search applied problem This advantages few parameters achieved ...

In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum Clustering for Feature Selection performs the selection in two steps. Partitioning the original features space in order to group similar features is performed using the Quantum Clustering algorithm. Then the selection of a representative for each cluster is carried out. It uses similarity measure...

The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In real world problems FS is a must due to the abundance of noisy, irrelevant or misleading features. However, current methods are inadequate at finding optimal reductions. This chapter presents a feature selection mecha...

Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology geographic information, multiview learning can be used to realize information fusion for better fault-cause identification. To reduce redundant different this paper, hierarchical feature selection (HMVFS) method is proposed...