Cover Image

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction.

Saved in:

Bibliographic Details
Main Authors:	Wah, Yap Bee, Ismail, Azlan, Azid, Naslina, Niswah, Nur, Jaafar, Jafreezal, Aziz, Izzatdin Abdul, Hasan, Mohd Hilmi, Zain, Jasni Mohamad
Format:	Article
Published:	2023
Online Access:	http://scholars.utp.edu.my/id/eprint/36742/
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction.
by: Wah, Yap Bee, et al.
Published: (2023)

Enhancing Accuracy Of Credit Scoring Classification With Imbalance Data Using Synthetic Minority Oversampling Technique-Support Vector Machine (SMOTE-SVM) Model
by: Bingamawa, Muhammad Tosan
Published: (2017)

An extended oversampling method for imbalanced Quranic text classification based on a genetic algorithm
by: Arkok, Bassam, et al.
Published: (2023)

Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
by: Mumtazimah, Mohamad, et al.
Published: (2019)

Improved adaptive semi-unsupervised weighted oversampling (IA-SUWO) using sparsity factor for imbalanced datasets
by: Ali, Haseeb
Published: (2019)

Support vector machine for solving imbalanced dataset problem
by: Mohd. Khairuddin, Ismail
Published: (2012)

Evaluation of machine learning techniques for imbalanced data in IDS
by: Mokaramian, Shahram
Published: (2013)

Oversampling analog-to-digital converter
by: Ma, Li Ya, et al.
Published: (2011)

Ensemble synthesized minority oversampling-based generative adversarial networks and random forest algorithm for credit card fraud detection.
by: Ghaleb, Fuad A., et al.
Published: (2023)

Motion pattern-based scene classification using adaptive synthetic oversampling and fully connected deep neural network.
by: Mohammed, Mohammed Sultan, et al.
Published: (2023)

Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
by: Amri, A’inur A’fifah, et al.
Published: (2018)

Evaluating oversampling techniques for network intrusion detection data
by: Chan, Jia Lin
Published: (2022)

Prediction of Machine Failure by Using Machine Learning Algorithm
by: Fakhrurazi, Nur Amalina
Published: (2019)

Improved class binarization model with data oversampling in gait recognition
by: Abdul Raziff, Abdul Rafiez
Published: (2019)

An enhanced machine learning framework for Type 2 diabetes classification using imbalanced data with missing values
by: Roy, Kumarmangal, et al.
Published: (2021)

GOS: a Genetic OverSampling Algorithm for classification of Quranic verses
by: Arkok, Bassam, et al.
Published: (2022)

A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
by: Mohd Pozi, Muhammad Syafiq
Published: (2016)

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets
by: Mohd Razali, Muhamad Hasbullah, et al.
Published: (2021)

Machine translation of minority languages : pivot language approach in Iban-English translation
by: Chin, Beatrice
Published: (2016)

Design and simulation of an on-chip oversampling converter with a CMOS-MEMS differential capacitive sensor
by: Ma, Li Ya, et al.
Published: (2011)

Oversampling based on data augmentation in convolutional neural network for silicon wafer defect classification
by: Batool, Uzma, et al.
Published: (2020)

Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
by: Amirruddin, Amiratul Diyana, et al.
Published: (2022)

Failure Model Prediction Using Machine Learning Techniques
by: BZaki, Imanul ‘Aqilah
Published: (2022)

Adaptive generation-based approaches of oversampling using different sets of base and nearest neighbor's instances
by: Nabus, Hatem S. Y., et al.
Published: (2022)

Dimension reduction and classifier-based feature selection for oversampled gene expression data and cancer classification
by: Petinrin, Olutomilayo Olayemi, et al.
Published: (2023)

Overview of support vector machine in modeling machining performances
by: Mat Deris, Ashanira, et al.
Published: (2011)

Sparkplug failure detection using Z-freq and machine learning
by: Ngatiman, Nor Azazi, et al.
Published: (2021)

Exploring the Common Failures and Routine Maintenance of Jeans Overlocking Machine
by: Yarui, Huo, et al.
Published: (2023)

A probabilistic classifier for imbalanced dataset problems
by: Lai, Yee Yang
Published: (2012)

Classification of imbalanced datasets using naive bayes
by: Mohd. Sobran, Nur Maisarah
Published: (2011)

Ensemble classifier and resampling for imbalanced multiclass learning
by: Sainin, Mohd Shamrie, et al.
Published: (2015)

An enhanced resampling technique for imbalanced data sets
by: Maisarah, Zorkeflee
Published: (2015)

Survey on highly imbalanced multi-class data
by: Abdul Hamid, Mohd Hakim, et al.
Published: (2022)

Risks of divorce: comparison between Cox and Parametric Models / Sanizah Ahmad ... [et al.]
by: Ahmad, Sanizah, et al.
Published: (2015)

Predictive Analytic on Machine Failure by Utilizing Linear Regression (Historical Data)
by: MOHD SAMLAN, NUR NASHA AYUNI
Published: (2020)

Enhancing manufacturing process by predicting component failures using machine learning
by: Saadat, Raihanus, et al.
Published: (2022)

Anomaly Detection with Machine Learning in the Presence of Extreme Value-A Review Paper
by: Suboh, Syahirah, et al.
Published: (2020)

Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA
by: Mohd. Zain, Azlan, et al.
Published: (2011)

A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
by: Mohd Pozi, Muhammad Syafiq, et al.
Published: (2015)

ANN in modeling the machining process
by: Mohd. Zain, Azlan, et al.
Published: (2008)