K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results

Martínez-Otzeta, José María; Sierra, Basilio; Lazkano, Elena; Astigarraga, Aitzol

doi:10.1007/11677437_5

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…In this study, we used three classification methods for testing: support vector machine [30], k-Nearest Neighbor [31] and decision tree [32]. For each testing, we configure the network layer, extract the features, and make classification using each method above.…”

Section: Classification Methodsmentioning

confidence: 99%

Convolutional neural network for maize leaf disease image classification

Syarief

Setiawan

2020

TELKOMNIKA

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This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.

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Section: Classification Methodsmentioning

confidence: 99%

Convolutional neural network for maize leaf disease image classification

Syarief

Setiawan

2020

TELKOMNIKA

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“…Nowadays, the standard machine learning methods are commonly used for classification like support vector machines (SVM), 10 K nearest neighbor (KNN), 11 and decision tree. 12 Most of the classification methods perform in three ways: supervised learning, 13 semisupervised learning, 14 and unsupervised learning. 15 The previous works conclude that a large number of labeled data are required in supervised learning, which implies that the experiment requires a high cost for artificial annotation.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, the standard machine learning methods are commonly used for classification like support vector machines (SVM), 10 K nearest neighbor (KNN), 11 and decision tree 12 . Most of the classification methods perform in three ways: supervised learning, 13 semi‐supervised learning, 14 and unsupervised learning 15 .…”

Section: Introductionmentioning

confidence: 99%

An ensemble model of convolution and recurrent neural network for skin disease classification

Ahmad

Usama

Ahmad

et al. 2021

Int J Imaging Syst Tech

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Skin cancer is one of the rapidly growing diseases in the world. Especially, millions of cases are reported every year by all types of skin cancer in America. Early detection of skin cancer using dermoscopy, the light source, and the magnification device are used to inspect the skin lesions. A dermatologist observed hypodermic structures are normally invisible. However, accurate and effective skin disease classification by humans is not straightforward and requires a long time of practice.

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K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results

Cited by 2 publications

References 14 publications

Convolutional neural network for maize leaf disease image classification

Convolutional neural network for maize leaf disease image classification

An ensemble model of convolution and recurrent neural network for skin disease classification

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