Cover Image

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

The conventional method to quantify leaf biochemical properties (nutrients and chlorophylls) is tedious, labour-intensive, and impractical for vast oil palm plantation areas. Spectral analysis retrieved from a spectroradiometer and an unmanned aerial vehicle (UAV) and imbalanced approaches such as t...

Full description

Saved in:

Bibliographic Details
Main Authors:	Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Tan, Ngai Paing, Ismail, Mohd Firdaus
Format:	Article
Published:	Elsevier 2022
Online Access:	http://psasir.upm.edu.my/id/eprint/103414/ https://www.sciencedirect.com/science/article/pii/S0168169921006633
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AK-means geometric smote with data complexity analysis for imbalanced dataset
by: Nur Athirah, Azhar
Published: (2023)

Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
by: Amirruddin, Amiratul Diyana, et al.
Published: (2020)

Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
by: Sayed Daud, Syarifah Noor Syakiylla, et al.
Published: (2023)

Fuzzy and smote resampling technique for imbalanced data sets
by: Zorkeflee, Maisarah, et al.
Published: (2015)

Chlorophyll-a mapping from the moderate resolution imaging spectroradiometer data in the South China Sea
by: Kueh, Hsiao Chin, et al.
Published: (2005)

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)

Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17
by: Amirruddin, Amiratul Diyana, et al.
Published: (2020)

LMT 100N - BAHASA INGGERIS PERSEDIAAN APRIL 08.
by: PBT, Pusat Bahasa & Terjemahan
Published: (2008)

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

Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
by: Saeed, Sana
Published: (2019)

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
by: Amri, A’inur A’fifah, et al.
Published: (2019)

Classification of oil palm nitrogen status from SPOT-6 satellite using support vector machine and spectral indices
by: Amirruddin, Amiratul Diyana, et al.
Published: (2017)

Evaluation of vegetation index (VI) in estimating nitrogen nutrition status in oil palm
by: Amirruddin, Amiratul Diyana, et al.
Published: (2015)

Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices
by: Amirruddin, Amiratul Diyana, et al.
Published: (2019)

Classification of Quranic topics using SMOTE technique
by: Arkok, Bassam, et al.
Published: (2021)

Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models
by: Tan, S. C., et al.
Published: (2011)

A multiple input probabilistic classifier for imbalanced dataset problem in semiconductor manufacturing
by: Lim, Chun Chew, et al.
Published: (2011)

Data sampling methods on imbalanced datasets for pneumonia detection in covid-19 patients
by: Dzulkefli, Syasya Farina
Published: (2022)

Deep learning-based high performance intrusion detection system for imbalanced datasets
by: Assaig, Faisal Ahmed, et al.
Published: (2023)

PM10 Retrieval From Spectroradiometer Measurements.
by: H, S Lim, et al.
Published: (2005)

Hybrid artificial neural network-naive bayes classifier for solving imbalanced dataset problems
by: Adam, Asrul
Published: (2012)

Artificial neural network — Naïve bayes fusion for solving classification problem of imbalanced dataset
by: Adam, A., et al.
Published: (2011)

A two-step supervised learning artificial neural network for imbalanced dataset problems
by: Adam, Asrul, et al.
Published: (2012)

An improved two-step supervised learning artificial neural network for imbalanced dataset problems
by: Che Shamsudin, Hasrul, et al.
Published: (2011)

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)

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

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

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

Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset
by: Amri, A’inur A’fifah, et al.
Published: (2017)

An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets
by: Azlin, Ahmad, et al.
Published: (2021)

An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets
by: Ahmad, Azlin, et al.
Published: (2021)

Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
by: Amirruddin, Amiratul Diyana, et al.
Published: (2017)

Deep LSTM model for diabetes prediction with class balancing by SMOTE
by: Suja A. Alex, et al.
Published: (2022)

Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
by: Amri, A’inur A’fifah, et al.
Published: (2016)

A two-step learning artificial neural network for solving an imbalanced dataset problem in semiconductor manufacturing
by: Adam, Asrul, et al.
Published: (2011)

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

Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
by: Shirley, Rufus, et al.
Published: (2023)

Development of a hybrid artificial neural network - naive bayes classifier for binary classification problem of imbalanced datasets
by: Adam, Asrul, et al.
Published: (2011)

Development of a hybrid artificial neural network - ive bayes classifier for biry classification problem of imbalanced datasets
by: Adam, Asrul, et al.
Published: (2011)