Improved Double-Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism - Geqiao Liu; Mingjie Tan (Complexity)
Multilabel classification is a key research topic in the machine learning field. In this study, the author put forward a two/two-layer chain classification algorithm with optimal sequence based on the attention mechanism. This algorithm is a classification model with a two-layer structure. By introducing an attention mechanism, this study analyzes the key attributes to achieve the goal of classification. To solve the problem of algorithm accuracy degradation caused by the order of classifiers, we adopt the OSS (optimal sequence selection) algorithm to find the optimal sequence of tags. The test results based on the actual dataset show that the ATDCC-OS algorithm has good performance on all performance evaluation metrics. The average accuracy of this algorithm is over 80%. The microaverage AUC performance reaches 0.96. In terms of coverage performance, its coverage performance is below 10%. The comprehensive result of single error performance is the best. The loss performance is about 0.03. The purpose of the ATDCC-OS algorithm proposed in the study is to help improve the accuracy of multilabel classification so as to obtain more effective data information.