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Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient...
Anomaly detection technique play an extraordinary role in the Intrusion Detection System (IDS) for its ability to detect novel attacks. To overcome the high-dimensionality problem the anomaly detection cursed of, we propose a novel Meta-Heuristic-based Sequential Forward Selection (MH_SFS) feature selection algorithm, which can be generally implemented in anomaly detection system. It is an improvement...
Blind measurement of visual quality is of fundamental importance in numerous image and video processing applications. Most of the no-reference Image Quality Assessment (NR IQA) methods are distortion-specific and their application domain is limited. Also, almost all distortion-generic NR IQA are computationally complex, making their applicability in real time applications very limited. In this paper...
When the customer churn prediction model is built, a large number of features bring heavy burdens to the model and even decrease the accuracy. This paper is aimed to review the feature selection, to compare the algorithms from different fields and to design a framework of feature selection for customer churn prediction. Based on the framework, the author experiment on the structured module with some...
Simba is a recently proposed algorithm based on hypothesis-margin for feature selection, it uses maximizing hypothesis-margin as a criterion for evaluating the effectiveness of a feature subset, in this way an effective feature subset can be efficiently obtained by employing the stochastic gradient ascent strategy. However, this algorithm still can not eliminate completely those redundant features...
Feature weighting can be considered an extension of feature selection. Traditional methods of feature weighting assume that feature relevance is invariant over the task's domain. As a result, they learn a single set of weight for the entire data set. In this paper, a proposed algorithm has been used, which is called simultaneous clustering and attribute discrimination (SCAD) and performs clustering...
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