FEAST: Feature evaluation and selection technique for deployment in unsupervised nonparametric environments

In the field of radar emitter recognition, with the wide application of modern radar, the traditional recognition method based on typical five feature parameters cannot achieve satisfactory recognition results in a complex electromagnetic environment. Currently, many new feature extraction methods are presented, but few approaches have been applied for feature evaluation or performance comparison. To deal with this problem, a feature evaluation and selection method was proposed based on set pair analysis (SPA) theory and analytic hierarchy process (AHP). The main idea of this method is to use SPA theory to solve problems regarding the construction of the decision matrix based on AHP, as it relies heavily on expert’s subjective experience. The aim was to improve the objectivity of the evaluation. To check the effectiveness of the proposed method, six feature parameters were selected for a comprehensive performance evaluation. Then, the convolutional neural network (CNN) was introduced to validate the recognition capability based on the evaluation results. Simulation results demonstrated that the proposed method could achieve the feature analysis and evaluation more reasonably and objectively.

Download Full-text

Feature evaluation and selection for condition monitoring using a self-organizing map and spatial statistics

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060417000518 ◽

2018 ◽

Vol 33 (1) ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Rui G. Silva ◽

Steven J. Wilcox

Keyword(s):

Neural Networks ◽

Tool Wear ◽

Spatial Statistics ◽

Condition Monitoring ◽

Self Organizing Map ◽

Feature Evaluation ◽

Feature Evaluation And Selection ◽

Combined Technique ◽

Self Organizing

AbstractThis paper presents a novel approach to sensor-based feature evaluation and selection using a self-organizing map and spatial statistics as a combined technique applied to tool condition monitoring of the turning process. This approach takes advantage of the unique features of unsupervised neural networks combined with spatial statistics to perform analyses into the contributions of the different sensor-based features, carrying large quantities of noise, to achieve a classification of tool wear and a quantitative measure of each feature's suitability. This method does not assume a prior direct correlation between features avoiding misconstructions inherent to common approaches that assume that only obviously correlated features should be considered for condition monitoring. Instead, and taking advantage of neural networks ability to perform non-linear modeling, it has allowed a prior modeling of the process and then analyzed each feature's contribution toward classification. It was found that some of the commonly used features have proven to have a significant contribution to the classification of cutting tool wear, whereas others adversely affect classification performance. Further, it is demonstrated that the proposed combined technique can be used extensively to quantitatively evaluate the contribution of different features toward system monitoring in the presence of noisy data.

Download Full-text