Recommendation System with Biclustering

Sun, Jianjun

doi:10.26599/bdma.2022.9020012

Cited by 6 publications

(1 citation statement)

References 31 publications

(40 reference statements)

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“…With biclustering, much local information can be extracted to improve recognition accuracy. Biclustering has been used in many other fields such as recommendation system [19], missing value prediction [20] and gene expression data analysis [21]. Typical methods include greedy search based methods [21], and model based methods [22].…”

Section: )Why and How To Use Local Features For Mobile Gas Recognition?mentioning

confidence: 99%

MGRBA: Gas Recognition With Biclustering and Adaboost

Zhou,

Wang,

Huang

et al. 2024

IEEE Access

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Gas recognition has been widely used in many fields such as air quality monitoring in dangerous areas. However, existing recognition methods suffer from two limitations: first, the recognition accuracy is not high. Due to the stochastic nature of air turbulence, gas features are not steady. The global features are sensitive to feature variations. Existing methods are based on global similarity, ignoring local similarity. Samples may be dissimilar in respect of global similarity, but are similar in terms of local similarity; Second, most existing recognition methods are based on the closed-set assumption that the gases categories in the train and test set are same. However, in real world applications, the test set may have non-overlapping gas category with the train set. To address above limitations, biclustering is used to extract local similarity. However, original biclustering method is not suitable for extraction. Since original biclustering method is used to find all kinds of biclusters, here we just want to find column nearly constant bicluster Therefore, a modified biclustering method is proposed. The local similarity can be used to construct classifier to recognize gas with adaboost. However, original adaboost cannot be used for open-set recognition. Thus a modified adaboost that uses two thresholds is proposed to recognize the unknown gases. To assess the efficacy of the proposed method, it is tested on public dataset. Experiment results demonstrate that the proposed method outperforms several state-of-the-art methods in respect of several evaluation measures on both closed-set and open-set cases.

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Section: )Why and How To Use Local Features For Mobile Gas Recognition?mentioning

confidence: 99%