Summary of Leveraging Semi-supervised Learning to Enhance Data Mining For Image Classification Under Limited Labeled Data, by Aoran Shen et al.
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
by Aoran Shen, Minghao Dai, Jiacheng Hu, Yingbin Liang, Shiru Wang, Junliang Du
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to improve data mining algorithms by incorporating semi-supervised learning methods. The goal is to enhance the ability to utilize unlabeled data, leading to more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, the study combines self-training with convolutional neural networks (CNNs) for image feature extraction and classification, iteratively improving model prediction performance. Experimental results on the CIFAR-10 dataset demonstrate that the proposed method outperforms traditional machine learning techniques like SVM, XGBoost, and MLP, yielding notable improvements in accuracy, recall, and F1 score. Additionally, the study validates the robustness and noise-resistance capabilities of the semi-supervised CNN model through experiments under varying noise levels, confirming its practical applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding better ways to analyze big data. Right now, traditional methods are not good enough for dealing with massive amounts of complex data. When we don’t have many labeled examples, these methods get even worse. The researchers propose a new approach called semi-supervised learning that can use both labeled and unlabeled data. They test this method using images and compare it to other common machine learning techniques. The results show that their approach is much better at recognizing patterns and making accurate predictions. This could be really useful in real-world situations where we need to make decisions based on large amounts of data. |
Keywords
» Artificial intelligence » Classification » Cnn » F1 score » Feature extraction » Machine learning » Pattern recognition » Recall » Self training » Semi supervised » Xgboost
