Joint multilabel classification and feature selection based on deep canonical correlation analysis

Abstract

In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

“…More precisely, the fault information of bearings includes multiple fault labels, such as the presence or absence of a fault, the position where the fault occurs or the manifestation of the fault. Multilabel classification algorithms can be divided into two categories: problem transformation and algorithm adaptation (Dai et al , 2020). Most problem transformation methods can be divided into three categories: binary relevance, classifier chain, and label power set (Zhang and Zhou, 2014).…”