Research On Multi-label Classification And Its Application In Logistics Experts Recommendation

The rapid development of information technology has brought about the "big data era". Big data contains a large scale of information, which results in information overload. Data mining technologies have been introduced to help get specific information from the mass data. Multi-label learning is a research hotspot in data mining, which can effectively solve the practical application of classification. Therefore, multi-label learning received more and more attention nowadays.Currently, researchers have focused on theory and application of multi-label learning. They have proposed several multi-label learning algorithms. However, challenges in applying them in different applications domains still exist. One major challenge is the high-dimensional data. In order to solve this challenge, this research proposed new multi-label learning algorithms based on Random subspace. First, research status on multi-label learning was analyzed, which clarifies the research problems and future directions. Secondly, the basic theory of multi-label learning, including machine learning, supervised learning, and multi-label learning were analyzed. Finally, for instability of classification chain and the high complexity of ensemble classification chains, this research constructed a new RS-CC and RS-ECC methods. The proposed methods were applied into logistics experts recommendation. Experimental results indicated that proposed methods achieved good results.In summary, this research made contributions to both theory and practice. On one hand, the theory of multi-label learning has been analyzed and enriched. On the other hand, this research applied the multi-label learning algorithms based on random subspace in logistics experts recommendation, which has expanded the application range of multi-label learning, and provided a new method for logistics experts recommendation.