ML-SLSTSVM: một máy vector hỗ trợ đôi có cấu trúc và phương pháp bình phương nhỏ nhất mới cho học đa nhãn

Pattern Analysis and Applications - Tập 23 - Trang 295-308 - 2019
Meisam Azad-Manjiri1, Ali Amiri1, Alireza Saleh Sedghpour2
1Department of Computer Engineering, University of Zanjan, Zanjan, Iran
2Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

Tóm tắt

Học đa nhãn (MLL) là một nhiệm vụ học có giám sát đặc biệt, trong đó một thể hiện đơn có thể thuộc về nhiều lớp đồng thời. Ngày nay, các phương pháp MLL ngày càng được yêu cầu nhiều trong các ứng dụng hiện đại, chẳng hạn như phân loại chức năng protein, nhận diện giọng nói và phân loại dữ liệu văn bản. Trong bài báo này, chúng tôi giới thiệu một bộ phân loại sử dụng máy vector hỗ trợ đôi có cấu trúc và phương pháp bình phương nhỏ nhất (SLSTSVM) cho học đa nhãn. Bộ phân loại ML-SLSTSVM được đề xuất tập trung vào thông tin cấu trúc dựa trên cụm của các lớp tương ứng trong mỗi bài toán tối ưu hóa, điều này là vô cùng quan trọng cho việc thiết kế một bộ phân loại tốt trong những vấn đề thực tiễn khác nhau. Phương pháp này được mở rộng thành một phiên bản phi tuyến bằng cách sử dụng thủ thuật hạt nhân. Kết quả thử nghiệm cho thấy phương pháp được đề xuất vượt trội về hiệu suất tổng quát so với các bộ phân loại khác.

Từ khóa

#học đa nhãn #máy vector hỗ trợ #phương pháp bình phương nhỏ nhất #phân loại #thuật toán phi tuyến

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