Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Grey Wolf Optimizer

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, enc...

Audio Feature Selection Based on Rough Set

Keeping audio features is important for audio index. However, in most cases the features number is huge, thus direct processing is time-consuming. Feature selection, as a preprocessing step of data mining, has turned to be very efficient in reducing dimensionality and removing irrelevant data. In this paper, we propose a feature selection algorithm based on Rough Set theory, which could find ou...

Blind Voice Separation Based on Empirical Mode Decomposition and Grey Wolf Optimizer Algorithm

Blind voice separation refers to retrieve a set of independent sources combined by an unknown destructive system. The proposed separation procedure is based on processing of the observed sources without having any information about the combinational model or statistics of the source signals. Also, the number of combined sources is usually predefined and it is difficult to estimate based on the ...

Best Emotional Feature Selection Criteria Based on Rough Set Theory

Non-verbal communication may be used to enhance verbal communication or even provide developers with an alternative for communicating information. Emotion or Gesture recognition is been highlighted in the area of Artificial Intelligence and advanced machine learning. Emotion or gesture is an important feature for an intelligent Human Computer Interaction. This paper basically is a literature su...

Incremental Feature Selection Based on Rough Set Theory (PhD Proposal)