A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection [PDF]
, 2015 This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based ...
Suwimol Jungjit +3 more
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Simpler is better: a novel genetic algorithm to induce compact multi-label chain classifiers [PDF]
, 2015 Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. One of the most effective MLC methods is known as Classifier Chains (CC).
Plastino, Alexandre +5 more
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Active learning with label correlation exploration for multi‐label image classification
IET Computer Vision, 2017 Multi‐label image classification has attracted considerable attention in machine learning recently. Active learning is widely used in multi‐label learning because it can effectively reduce the human annotation workload required to construct high ...
Jian Wu +5 more
doaj +1 more source
A Survey on Multi-Label Data Stream Classification
IEEE Access, 2020 Nowadays, many real-world applications of our daily life generate massive volume of streaming data at a higher speed than ever before, to name a few, Web clicking data streams, sensor network data and credit transaction streams.
Xiulin Zheng +3 more
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Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
Information, 2019 Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency ...
Yange Sun, Han Shao, Shasha Wang
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Multi-Label Image Classification Based on Label Visual Prototype Learning [PDF]
Jisuanji gongchengMulti-label image classification studies tend to use label semantic information and label co-occurrence probability as prior knowledge to guide the learning of multi-label classification models.
LI Jiao, FAN Haodong, HONG Xudong, XU Zhenyi, FAN Xu, HUANG Jun
doaj +1 more source
A novel multi-label classification algorithm based on -nearest neighbor and random walk
International Journal of Distributed Sensor Networks, 2020 The multi-label classification problem occurs in many real-world tasks where an object is naturally associated with multiple labels, that is, concepts.
Zhen-Wu Wang +3 more
doaj +1 more source
Multi-label classification of music by emotion [PDF]
EURASIP Journal on Audio, Speech, and Music Processing, 2011 This work studies the task of automatic emotion detection in music. Music may evoke more than one different emotion at the same time. Single-label classification and regression cannot model this multiplicity. Therefore, this work focuses on multi-label classification approaches, where a piece of music may simultaneously belong to more than one class ...
Konstantinos Trohidis +3 more
openaire +1 more source
Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [PDF]
Jisuanji kexue, 2022 Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of ...
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
doaj +1 more source
Unsupervised multi-label text classification using a world knowledge ontology [PDF]
, 2012 The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories
Hua Wang +8 more
core +2 more sources