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Multi-Label Classification With Label-Specific Feature Generation : A Wrapped Approach

Label-specific features serve as an effective strategy to learn from multi-label data, where a set of features encoding specific characteristics of each label are generated to help induce multi-label classification model. Existing approaches work by taking the two-stage strategy, where the procedure of label-specific feature generation is independent of the follow-up procedure of classification model induction. Intuitively, the performance of resulting classification model may be suboptimal due to the decoupling nature of the two-stage strategy. In this paper, a wrapped learning approach is proposed which aims to jointly perform label-specific feature generation and classification model induction. Specifically, one (kernelized) linear model is learned for each label where label-specific features are simultaneously generated within an embedded feature space via empirical loss minimization and pairwise label correlation regularization. Comparative studies over a total of sixteen benchmark data sets clearly validate the effectiveness of the wrapped strategy in exploiting label-specific features for multi-label classification.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.9 Part 2 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 5199-5211

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MULTI-LABEL CLASSIFICATION
LABEL-SPECIFIC FEATURES
LABEL CORRELATION
WRAPPED PROCEDURE

Info Detail Spesifik

DOI: 10.1109/TPAMI.2021.3070215

Pernyataan Tanggungjawab

Ze-Bang Yu, Min-Ling Zhang

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