Application of Unsupervised Feature Selection in Cashmere and Wool Fiber Recognition

ABSTRACTSuitable features are the key to identifying cashmere and wool fibers, and feature selection is an important step in classification. Existing supervised feature selection methods need to consider the information between fiber features and class labels. Aiming at making up for this deficiency, we propose an unsupervised feature selection method based on k-means clustering, which overcome the difficulty that fiber feature class labels are either unavailable or costly to obtain. Firstly, the subset of fiber features that have been normalized are clustered by the k-means clustering algorithm to obtain the total number of clusters, and the clustering effect is evaluated by the DB Index criterion. Next, the DB value of each feature subset, the correlation of features and the total number of the clustering are considered as the judgment criteria to select the optimal feature subset. Finally, the optimal subset of features obtained by unsupervised feature selection algorithms is fed into a support vector machine for automatic identification and classification of the two fibers. The experimental results show that the method achieves a high recognition rate of 97.25%. It is verified that the unsupervised feature selection method based on k-means clustering is effective for the recognition of cashmere and wool.