Evaluating the impact of feature selection consistency in software prediction

2022 ◽  
Vol 213 ◽  
pp. 102715
Author(s):  
Asad Ali ◽  
Carmine Gravino
Keyword(s):  
Feature Selection ◽  
The Impact ◽  
Selection Consistency

A comprehensive investigation of the impact of feature selection techniques on crashing fault residence prediction models

2021 ◽  
pp. 106652
Author(s):  
Kunsong Zhao ◽  
Zhou Xu ◽  
Meng Yan ◽  
Tao Zhang ◽  
Dan Yang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Comprehensive Investigation ◽  
The Impact ◽  
Feature Selection Techniques

scholarly journals Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarv Priya ◽  
Tanya Aggarwal ◽  
Caitlin Ward ◽  
Girish Bathla ◽  
Mathews Jacob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Hypertension ◽  
Feature Selection ◽  
Subgroup Analysis ◽  
Cardiac Mri ◽  
Intraclass Correlation ◽  
Poor Performance ◽  
Superior Performance ◽  
Feature Filtering ◽  
The Impact

AbstractSide experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.

Investigating the Impact of Similarity Metrics in an Unsupervised-Based Feature Selection Method

2017 ◽  
Author(s):  
Carine A. Dantas ◽  
Romulo de O. Nunes ◽  
Anne M.P. Canuto ◽  
Jolao C. Xavier-Junior
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Similarity Metrics ◽  
The Impact

scholarly journals Feature Selection Optimization for Highlighting Opinions Using Supervised and Unsupervised Learning on Arabic Language

2021 ◽  
Vol 10 (2) ◽  
pp. 636-642
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Unsupervised Learning ◽  
Arabic Language ◽  
Machine Learning Techniques ◽  
Business Decision ◽  
Supervised And Unsupervised Learning ◽  
Proposed Model ◽  
The Impact

Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four machine learning techniques on the same three Arabic language corpora. This paper aims at increasing the accuracy of opinion highlighting on Arabic language, by using enhanced feature selection approaches. FS proposed model is adopted for enhancing opinion highlighting purpose. The experimental results show the outperformance of the proposed approaches in variant levels of supervisory,i.e. different techniques via distinct data domains. Multiple levels of comparison are carried out and discussed for further understanding of the impact of proposed model on several ML techniques.

scholarly journals Impact of Feature Selection on Clustering Images ofVertebral Compression Fractures

2020 ◽  
Author(s):  
Raquel Candido ◽  
Rafael Lama ◽  
Natália Chiari ◽  
Marcello Nogueira-Barbosa ◽  
Paulo Azevedo Marques ◽  
...  
Keyword(s):  
Feature Selection ◽  
Metastatic Cancer ◽  
Vertebral Compression Fractures ◽  
Features Selection ◽  
Correct Classification ◽  
Compression Fractures ◽  
Vertebral Bodies ◽  
The Impact ◽  
Aided Diagnosis

Non-traumatic Vertebral Compression Fractures (VCFs) are generally caused by osteoporosis (benign VCFs) or metastatic cancer (malignant VCFs) and the success of the medical treatment strongly depends on a fast and correct classification of VCFs. Recently, methods for computer-aided diagnosis (CAD) based on machine learning have been proposed for classifying VCFs. In this work, we investigate the problem of clustering images of VCFs and the impact of feature selection by genetic algorithms, comparing the clustering i)with all features and ii)with feature selection through the purity results. The analysis of the clusters helps to understand the results of classifiers and difficulties of differentiating images of different classes by an expert. The results indicate that features selection improved the separability of clusters and purity. Feature selection also helps to understand which attributes are most important for analysing the images of vertebral bodies.

scholarly journals Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6793
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Attique Khan ◽  
Mussarat Yasmin ◽  
Jamal Hussain Shah ◽  
Marcin Gabryel ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Data Augmentation ◽  
Feature Fusion ◽  
Pearson Correlation ◽  
Document Classification ◽  
Document Image ◽  
Support Vector ◽  
Neural Network Models ◽  
The Impact

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.

Impact of feature selection on classification via clustering techniques in software defect prediction

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
F.E. Usman-Hamza ◽  
A.F. Atte ◽  
A.O. Balogun ◽  
H.A. Mojeed ◽  
A.O. Bajeh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Feature Selection Method ◽  
Predictive Performance ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Selection Methods ◽  
Clustering Techniques ◽  
Software Defect ◽  
The Impact

Software testing using software defect prediction aims to detect as many defects as possible in software before the software release. This plays an important role in ensuring quality and reliability. Software defect prediction can be modeled as a classification problem that classifies software modules into two classes: defective and non-defective; and classification algorithms are used for this process. This study investigated the impact of feature selection methods on classification via clustering techniques for software defect prediction. Three clustering techniques were selected; Farthest First Clusterer, K-Means and Make-Density Clusterer, and three feature selection methods: Chi-Square, Clustering Variation, and Information Gain were used on software defect datasets from NASA repository. The best software defect prediction model was farthest-first using information gain feature selection method with an accuracy of 78.69%, precision value of 0.804 and recall value of 0.788. The experimental results showed that the use of clustering techniques as a classifier gave a good predictive performance and feature selection methods further enhanced their performance. This indicates that classification via clustering techniques can give competitive results against standard classification methods with the advantage of not having to train any model using labeled dataset; as it can be used on the unlabeled datasets.Keywords: Classification, Clustering, Feature Selection, Software Defect PredictionVol. 26, No 1, June, 2019

scholarly journals The Impact of Feature Selection on Web Spam Detection

2012 ◽  
Vol 4 (9) ◽  
pp. 61-67 ◽  
Author(s):  
Jaber Karimpour ◽  
Ali A. Noroozi ◽  
Adeleh Abadi
Keyword(s):  
Feature Selection ◽  
Spam Detection ◽  
Web Spam ◽  
The Impact
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