REVIEW 9 cited by
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector Machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios.
Forward citations
Cited by 9 Pith papers
-
Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt
A ResNeXt-based multi-task learning model reportedly outperforms LSTM, Transformer, MCCNN, and DSN on S&P 500 classification and regression, but the experiments lack error bars, code, and leakage controls.
-
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
On a public chest X-ray dataset, VGG19 is reported to outperform SVM, XGBoost, MLP, and ResNet50 in accuracy, AUC, F1, and recall, but without a reproducible evaluation protocol.
-
Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer
A CNN-LSTM gesture recognizer with a decorative 3D skeleton visualization that reports unverifiable accuracy and speed numbers.
-
Dynamic Scheduling Strategies for Resource Optimization in Computing Environments
A weighted-sum container placement objective solved with a genetic algorithm is claimed to outperform static rules and heuristics on Google Cluster Data, but the comparison lacks methodology, baselines, and code.
-
Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization
A DQN-based reinforcement learning system is reported to reach CTR 0.78 and RR 0.83 on an unverified CLIP Interactions dataset, beating five baselines, but no reproducible evidence is provided.
-
Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining
An SVM-based frequent pattern mining method is claimed to outperform FP-Growth, FP-Tree, decision trees, and random forests, but the paper provides no reproducible experimental support.
-
Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders
A vanilla VAE trained on the RICO dataset is reported to beat AE, GAN, cGAN, DBN, and VAE-GAN on SSIM/MAE for UI generation, but the comparison is under-specified and the dynamic feedback claim is untested.
-
AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP
An XGBoost model with SHAP explanations is applied to edge node health classification, but the weak reported accuracy and missing experimental details do not support the paper's claims.
-
A Matrix Logic Approach to Efficient Frequent Itemset Discovery in Large Data Sets
The paper restates the standard Boolean matrix (vertical bit-vector) approach to frequent itemset mining and reports self-measured runtime and memory on the Groceries dataset without any baseline comparison.
Discussion (0). Continue with ORCID to comment.