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Label augmented and weighted majority voting for crowdsourcing - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper proposes a novel label integration method called label augmented and weighted majority voting (LAWMV) to infer the true label of each instance from its multiple noisy label set.
2. LAWMV uses the K-nearest neighbors (KNN) algorithm to find each instance’s K-nearest neighbors and merges their multiple noisy label sets to obtain its augmented multiple noisy label set.
3. The experimental results on 34 simulated and two real-world crowdsourced datasets show that LAWMV significantly outperforms all the other state-of-the-art label integration methods.
Article analysis:
This article is generally trustworthy and reliable, as it provides a detailed description of the proposed method, LAWMV, and presents evidence for its effectiveness through experiments on 34 simulated and two real-world crowdsourced datasets. The authors also provide an extensive review of related work in the field of label integration for inferring true labels from multiple noisy labels obtained from different crowd workers.
The article does not appear to be biased or one-sided, as it objectively presents both sides of the argument regarding the effectiveness of different methods for inferring true labels from multiple noisy labels obtained from different crowd workers. Furthermore, there are no unsupported claims or missing points of consideration in the article, as all claims are supported by evidence presented in the form of experiments conducted on various datasets. Additionally, there are no unexplored counterarguments or promotional content present in this article.
The only potential issue with this article is that it does not present both sides equally; while it provides an extensive review of related work in the field of label integration for inferring true labels from multiple noisy labels obtained from different crowd workers, it does not provide a similar level of detail regarding LAWMV itself. However, this is understandable given that this article focuses primarily on presenting evidence for LAWMV's effectiveness rather than providing an exhaustive overview of its features and capabilities.