A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation
Demirarslan, M.; Suner, A.
Show abstract
In disease diagnosis classification, ensemble learning algorithms enable strong and successful models by training more than one learning function simultaneously. This study aimed to eliminate the irrelevant variable problem with the proposed new feature selection method and compare the ensemble learning algorithms classification performances after eliminating the problems such as missing observation, classroom noise, and class imbalance that may occur in the disease diagnosis data. According to the findings obtained; In the preprocessed data, it was seen that the classification performance of the algorithms was higher than the raw version of the data. When the algorithms classification performances for the new proposed advanced t-Score and the old t-Score method were compared, the feature selection made with the proposed method showed statistically higher performance in all data sets and all algorithms compared to the old t-Score method (p = 0.0001).
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multi- Stage Feature Selection (MSFS) Algorithm for UWB- Based Early Breast Cancer Size Prediction 97%
- Cardiac disease diagnosis based on GAN in case of missing data 96%
- Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques 96%
Similar papers in this journal
- A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments 97%
- Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights 96%
- Unsupervised Discovery of Risk Profiles on Negative and Positive COVID-19 Hospitalized Patients 95%
Similar papers in this journal
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 96%
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 95%
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 95%
Similar papers in this journal
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 94%
- Identifying Protein Complexes in Protein-protein Interaction Data using Graph Convolution Network 93%
- Modeling and Visualization of Rice Root Based on Morphological Parameters 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.