Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

Quantum based Whale Optimization Algorithm for wrapper feature selection

(2020) R.K. Agrawal et al.   APPLIED SOFT COMPUTING

Hybrid-Recursive Feature Elimination for Efficient Feature Selection

(2020) Hyelynn Jeon et al.   Applied Sciences-Basel

The impact of automated feature selection techniques on the interpretation of defect models

(2020) Jirayus Jiarpakdee et al.   EMPIRICAL SOFTWARE ENGINEERING

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification

(2020) Alejandro Lopez-Rincon et al.   Cancers

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma

(2020) Nguyen Quoc Khanh Le et al.   Journal of Personalized Medicine

CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection

(2020) Xudong Du et al.   APPLIED SOFT COMPUTING

Classifier ensemble methods in feature selection

(2020) Hakan Ezgi Kiziloz   NEUROCOMPUTING

Machine learning techniques for code smell detection: A Systematic literature review and meta-Analysis

(2019) Muhammad Ilyas Azeem et al.   INFORMATION AND SOFTWARE TECHNOLOGY

A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

(2019) Kang Leng Chiew et al.   INFORMATION SCIENCES

Fast prediction of reservoir permeability based on EFS-LightGBM using direct logging data

(2019) Kai-Bo Zhou et al.   MEASUREMENT SCIENCE and TECHNOLOGY

Efficient feature selection techniques for sentiment analysis

(2019) Avinash Madasu et al.   MULTIMEDIA TOOLS AND APPLICATIONS

Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods

(2018) V.F. Rodriguez-Galiano et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors

(2018) Ramendra Prasad et al.   SOIL & TILLAGE RESEARCH

A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

(2018) Dahai Zhang et al.   IEEE Access

Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

(2017) Yang Liu et al.   EXPERT SYSTEMS WITH APPLICATIONS

Continuous quality assessment with inCode

(2017) George Ganea et al.   SCIENCE OF COMPUTER PROGRAMMING

Comparing and experimenting machine learning techniques for code smell detection

(2015) Francesca Arcelli Fontana et al.   EMPIRICAL SOFTWARE ENGINEERING

Feature Selection with theBorutaPackage

(2015) Miron B. Kursa et al.   Journal of Statistical Software

A survey on feature selection methods

(2013) Girish Chandrashekar et al.   COMPUTERS & ELECTRICAL ENGINEERING

On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed

(2013) Victoria López et al.   INFORMATION SCIENCES

Embedded Feature Selection for Multi-label Classification of Music Emotions

(2013) Mingyu You et al.   International Journal of Computational Intelligence Systems

Multi-class AdaBoost

(2013) Trevor Hastie et al.   Statistics and Its Interface

Code Bad Smells: a review of current knowledge

(2010) Min Zhang et al.   JOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION-RESEARCH AND PRACTICE

BDTEX: A GQM-based Bayesian approach for the detection of antipatterns

(2010) Foutse Khomh et al.   JOURNAL OF SYSTEMS AND SOFTWARE

A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

(2009) Bjoern H Menze et al.   BMC BIOINFORMATICS

DECOR: A Method for the Specification and Detection of Code and Design Smells

(2009) N. Moha et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

A systematic analysis of performance measures for classification tasks

(2009) Marina Sokolova et al.   INFORMATION PROCESSING & MANAGEMENT