Personalized Multi-label Classification for Incomplete Data
ZHU Mengxiao
DUAN Haochen
YUE Kun
ZHOU Feng
ZHU Mengjie
Abstract：In recent years,with the development of technologies such as machine learning and deep learning,multi-label classification techniques have become mature.However,existing multi-label classification methods often assume that data is readi-ly available and complete.In real-world scenarios,this assumption is frequently limited,as acquir-ing many datasets can be costly.To address this,a deep reinforcement learning based personalized multi-label classification framework(RLPMC)is proposed for in-complete data in real scenarios,considering the cost-based nature of data acquisition.This framework includes a feature encoder,feature selector,and multi-label classifier.First,to address the issue of missing values in incomplete data,the feature encoder based on set embedding converts variable-length data into fixed-length vectors,which are inputted into the multi-label classifier and feature selector.Next,a feature selector based on deep reinforcement learning is designed to learn personalized feature acquisi-tion strategies,balancing the cost of feature acquisition and classification accuracy.Then,based on the selected features,accurate classification is achieved using multi-label classification methods.Finally,multiple experiments on synthetic and public datasets validate the effectiveness of the approach.
Keywords：multi-label classificationincomplete datafeature acquisition strategiesdeep reinforcement learningfeature encoding
Publication Date：2025-08-20
Online Publishing Date：2025-12-12(First online date of this platform, not the publication date of the document)
Pages：7( 2057-2062,2088 )