Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Boateng, Ernest Yeboah; Otoo, J. A.; Abaye, Daniel A.

doi:10.4236/jdaip.2020.84020

Cited by 145 publications

(88 citation statements)

References 59 publications

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“…Machine learning methods, such as the support vector machine (SVM), artificial neural network (ANN), and random forest regression (RFR), have been widely used to retrieve vegetation parameters [16][17][18][19]. Nevertheless, training an SVM with high-dimensional data can be extremely slow [20], while ANN is prone to overfitting, and the parameter setting in ANN is more complicated [21]. Compared to SVM and ANN, RFR has proven to be a very robust machine learning algorithm for the retrieval of vegetation parameters, including the CCC [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, the retrieval models established using empirical methods or machine learning mostly use local sampled data for training. Although the empirical retrieval model is accurate for a specific type, a specific area, or a specific time period, its generalization is poor with regard to the time-space change [20]. In recent years, a new hybrid retrieval approach involving vegetation radiative transfer models (RTMs) and machine learning algorithms has been developed to compensate for limited field data [23].…”

Section: Introductionmentioning

confidence: 99%

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A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

et al. 2021

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Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

et al. 2021

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“…KNN is a powerful classification [10] algorithm well known for pattern recognition [11]. That is why it is used in this project to classify new objects (donations) based on similarity measurement between donations already shared with beneficiaries and new donations not shared yet.…”

Section: Knn Implementationmentioning

confidence: 99%

E-Service Application: Online Donation to Help Fight COVID-19

Nasr¹,

Jbeli²,

Smiti³

2021

ISI

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The Artificial Intelligence (AI) can promote research and find optimal solutions for complex and unstable situations. COVID-19 highlights the urgent need to innovate and offer modern solutions. Those solutions must meet the business requirement but also the current circumstances. In this paper, we are going to describe a new E-service application: Online Donation to Help Fight COVID-19. Our online donation software is perfect for nonprofits. The application has many features to suit our needs and their support response time. We use the Machine learning technique K-Nearest Neighbor to identify the ideal beneficiaries (school, hospital…). Our project can resolve the problem of donation management and establish the transparency and trust.

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“…In recent years, data-driven approaches and artificial intelligent algorithms have been in the area of focus due to their higher efficacy. Random forest (RF), Neural Networks (NNs), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Extreme Learning Machine (ELM) and Naïve Bayes (NB) are amongst such techniques used for landslide susceptibility mapping (Boateng et al 2020;Chen et al, 2019;Dou et al, 2019;Huang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Novel Ensemble-Based Machine Learning Models Based on The Bagging, Boosting and Random Subspace Methods for Landslide Susceptibility Mapping

Al-Fugara

Ahmadlou

Pradhan

2021

Preprint

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Indivisual machine learning models show different limitations such as low generalization power for modeling nonlinear phenomena with complex behavior. In recent years, one of the best approaches to this issue is to use ensemble models. The purpose of this paper is to investigate the predictive power and modeling of three novel ensemble models constructed with four machine learning models: Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB) models based on three approaches of Bagging, boosting and Random Subspace (RS) in landslide susceptibility mapping (LSM) in the Province of Ajloun in Jordan. A total number of 91 landslide locations along with 16 conditioning factors in LSM were identified and used. Also, before modeling, the selection of effective conditioning factors in LSM was done using genetic algorithm and four single models including DT, KNN, NB and SVM. The selected factors were used in modeling with individual and ensemble models. The results show that the area under the receiver operating characteristic curve (AUROC) for ensemble models is significantly higher than the individual models and the AUC for ensemble models was on average 14% higher than individual models. Based on the results, the most accurate models were RS ensemble model (AUROC = 0.850), Boosting (AUROC = 0.848) and Bagging (AUROC = 0.814), respectively. This study showed that by combining the results of simple machine learning models and making ensemble models, models with the desired accuracy can be achieved.

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Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Cited by 145 publications

References 59 publications

A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

E-Service Application: Online Donation to Help Fight COVID-19

Novel Ensemble-Based Machine Learning Models Based on The Bagging, Boosting and Random Subspace Methods for Landslide Susceptibility Mapping

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