Results 41 to 50 of about 881,675 (221)
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
doaj +1 more source
Large-Scale Multi-Label Learning with Incomplete Label Assignments [PDF]
, 2014 Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations.
Fan, Wei +6 more
core +2 more sources
Multi-Label Classification of E-Commerce Customer Reviews via Machine Learning
Axioms, 2022 The multi-label customer reviews classification task aims to identify the different thoughts of customers about the product they are purchasing. Due to the impact of the COVID-19 pandemic, customers have become more prone to shopping online.
Emre Deniz, H. Erbay, Mustafa Cosar
semanticscholar +1 more source
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
Scientometrics, 2021 Patent classification is an expensive and time-consuming task that has conventionally been performed by domain experts. However, the increase in the number of filed patents and the complexity of the documents make the classification task challenging. The
Arousha Haghighian Roudsari +3 more
semanticscholar +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
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
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
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning [PDF]
, 2014 We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
Aussem, Alex +2 more
core +6 more sources
Learning preferences for large scale multi-label problems [PDF]
, 2018 Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and ...
CW Hsu +8 more
core +2 more sources