Classification of pancreas tumor dataset using adaptive weighted k nearest neighbor algorithm

Kaya, Mahmut; Bilge, Hasan Şakir

doi:10.1109/inista.2014.6873626

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…k-NN terdiri dari dua tahap, yaitu: pelatihan dan ujian yang dilakukan dengan memeriksa jarak dari setiap sampel uji ke sampel pelatihan untuk mengklasifikasikan setiap sampel uji. Nilai k karus dipilih berdasarkan dataset yang memiliki kelas sedangkan metrik jarak adalah parameter penting lainnya yang digunakan untuk menghitung dekat atau jauhnya tetangga dari sampel uji [29].…”

Section: Tujuan Penelitianunclassified

“…Tahapan untuk algoritma k-NN dapat penulis urutkan yaitu: 1) Tentukan nilai K, 2) Hitung jarak antara data baru dan semua data latih yang sudah ada dengan metrik jarak, 3) Tentukan K label data yang mempunyai jarak yang paling minimal, 4) Klasifikasikan data baru kedalam label data yang mayoritas Ada berbagai metrik jarak pada k-NN namun metrik jarak Manhattan dan Euclidean merupakan matrik jarak yang paling banyak digunakan. Akurasi hasil dari kedua metrik jarak tersebut memperoleh hasil yang berbedabeda [29].…”

Section: Manfaat Penelitianunclassified

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Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Kurniawan¹,

Abror²

2019

Explore. jurnal. sistem. inf. dan. telematika

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The methods for Software Effort Estimation are divided into two, these methods are grouped into Non Machine Learning (non-ML) and Machine Learning (ML) methods [1]. The k-NN method has the disadvantage of being unable to tolerate irrelevant features and greatly affect the accuracy of k-NN. The k-NN method is also difficult to deal with missing data problems and feature categorization problems such as features that are not relevant, weight features that are not optimal, and the same features [2]. Whereas the dataset of Software Effort Estimation still has some serious challenges such as the characteristics of the data set, which are irrelevant features and the level of influence of each feature in the estimated data of the software effort [3]. This study compared the k-NN individual method with the combination of feature selection method with k-NN to find out which method was the best. The results showed that the Forward Selection (FS) method and Median Weighted Information Gain with k-Nearest Neighbor can overcome the problem of irrelevant features so as to increase accuracy in the RMSE Software Effort Estimation dataset, which is smaller in the Albrecht dataset of 5,953 using the Median method-WIG k-NN, the Miyazaki dataset is 55,421 and Kemerer is 123,081 using the FS k-NN method. The combination of kNN with Feature Selection is proven to be able to improve the estimation results better than kNN individuals. With the FS k-NN method being the best by winning in 2 datasets Miyazaki and Kemerer.

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Section: Tujuan Penelitianunclassified

Section: Manfaat Penelitianunclassified

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Kurniawan¹,

Abror²

2019

Explore. jurnal. sistem. inf. dan. telematika

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“…Using data from satellite photography, this research studies deep learning algorithms to identify deforested areas in forests and optimize these detections to yield reliable results. With a wide range of possible applications in fields including food and health, deep learning stands out as a significant technological advancement [6][7][8]. To successfully complete the segmentation task and deliver advanced results, many machine learning techniques can be applied [9].…”

Section: Introductionmentioning

confidence: 99%

Accurate Detection of Treeless Areas in Forests Using Deep Learning Models

2023

Proceeding Book of 2nd International Conference on Recent Academic Studies ICRAS 2023

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In order to maintain biological diversity, control climate, and manage sustainable resources, forests are an essential part of the global ecosystem. To improve the efficacy of forestry methods, estimate the effects of natural disasters, and fortify environmental legislation, it is crucial to monitor and classify forests. This study applies deep learning models, especially U-Net and Res-Net, to imagery from satellites to identify deforested areas within forests. The models were studied for segmentation, and the resulting data was compared. With 96.1% accuracy, 27% IoU, and 92.7% Dice Coefficient, the U-Net model performed quite well in identifying deforested areas inside of forests. As a result of these excellent results, U-Net architecture-based applications have the potential to positively impact a wide range of applications, from forestry to monitoring and predicting fires, assessing the consequences of natural disasters, and improving environmental policy.

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A Systematic Comparison and Evaluation of Supervised Machine Learning Classifiers Using Headache Dataset

Aljaaf

Al‐Jumeily

Hussain

et al. 2015

Lecture Notes in Computer Science

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Classification of pancreas tumor dataset using adaptive weighted k nearest neighbor algorithm

Cited by 7 publications

References 15 publications

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Accurate Detection of Treeless Areas in Forests Using Deep Learning Models

A Systematic Comparison and Evaluation of Supervised Machine Learning Classifiers Using Headache Dataset

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