A Query-Specific Representation-Based Sampling Strategy for Imbalanced Classification Article Swipe
Yi Tan
,
Yanpeng Qu
,
Longzhi Yang
,
Hong Bo Liu
,
Changjing Shang
,
Qiang Shen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.171073556.66443984/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.171073556.66443984/v1
Data imbalance is a common challenge for many classification tasks where the dataset suffers from disproportionate partitions of samples between classes. Typical techniques for handling class imbalance are either based on algorithmic or data-driven strategies. The algorithmic strategies reduce model bias towards the majority classes by modifying the classification algorithms, and the data-driven strategies revise the datasets in terms of resampling. The current implementations of the datadriven strategy often ignore the correlations between instances but assign a uniform sampled dataset to all queries despite their distinctions. This may eliminate valid instances and retain irrelevant instances during sampling, thereby negatively affecting classification performance. To address this limitation, this paper presents a representation-based sampling (RBS) approach, implemented in a bi-stage hybrid framework, including a training stage to compile the sampling dictionary and a retrieving stage to sample the data with respect to the query. In the first stage, RBS learns the correlations between each object per class via a reconstruction model, and produces a sampling dictionary for each class. In the second stage, for a given query, the sampled data specific to this query in each class are retrieved via the offline sampling dictionary by locating its most related object in each class. Because the number of the sampled data in each class are unified, the classification of the query is guaranteed to be conducted on a class-balance dataset. The systematic experiments uisng image datasets demonstrate that RBS can effectively solve the data imbalance issue in classification and improve the representation of images with correlated features, leading to a better recognition performance.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.171073556.66443984/v1
- https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.171073556.66443984
- OA Status
- gold
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392923161
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392923161Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.36227/techrxiv.171073556.66443984/v1Digital Object Identifier
- Title
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A Query-Specific Representation-Based Sampling Strategy for Imbalanced ClassificationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-18Full publication date if available
- Authors
-
Yi Tan, Yanpeng Qu, Longzhi Yang, Hong Bo Liu, Changjing Shang, Qiang ShenList of authors in order
- Landing page
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https://doi.org/10.36227/techrxiv.171073556.66443984/v1Publisher landing page
- PDF URL
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https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.171073556.66443984Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.171073556.66443984Direct OA link when available
- Concepts
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Representation (politics), Sampling (signal processing), Computer science, Artificial intelligence, Machine learning, Data mining, Political science, Law, Filter (signal processing), Computer vision, PoliticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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