ArticleSensors (Basel, Switzerland)2020
Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 143 citations in OpenAlex.
- Enhance autism spectrum disorder detection using stacking ensemble learning model with explainable AI.BioData mining · 2026Article
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- The associative pathways linking intangible cultural heritage short videos to youth rural cultural identity: An experiment based on eye-tracking and structural equation modeling.Frontiers in psychology · 2026Article
- Machine learning-based migraine analysis using retinal vessel diameters from optical coherence tomography: an alternative approach.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- A comprehensive dataset of soccer event images for advancing automatic recognition systems.Data in brief · 2025Article
- A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- A new design of wind power prediction method based on multi-interaction optimization informer model.PloS one · 2025Article
- Biomarker-driven drug repurposing for NAFLD-associated hepatocellular carcinoma using machine learning integrated ensemble feature selection.Frontiers in bioinformatics · 2025Article
- InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification.Frontiers in artificial intelligence · 2025Article
- Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.JMIR medical informatics · 2024Article
- Evaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey.Sensors (Basel, Switzerland) · 2024Article
- Article
- Interlimb and Intralimb Synergy Modeling for Lower Limb Assistive Devices: Modeling Methods and Feature Selection.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Explainable AI-driven model for gastrointestinal cancer classification.Frontiers in medicine · 2024Article
- Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures.Sensors (Basel, Switzerland) · 2023Article
- Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.Bioengineering (Basel, Switzerland) · 2023Article
- Early Fire Detection Using Long Short-Term Memory-Based Instance Segmentation and Internet of Things for Disaster Management.Sensors (Basel, Switzerland) · 2023Article
- Correlation-Based Anomaly Detection in Industrial Control Systems.Sensors (Basel, Switzerland) · 2023Article
- Prediction of the Critical Temperature of Superconductors Based on Two-Layer Feature Selection and the Optuna-Stacking Ensemble Learning Model.ACS omega · 2023Article
- Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning.Heliyon · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique's major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.