scholarly journals SELEÇÃO DE VARIÁVEIS PARA CATEGORIZAÇÃO DE AMOSTRAS QUÍMICAS

2017 ◽  
Vol 36 (4) ◽  
pp. 28
Author(s):  
M. J. Anzanello
Keyword(s):  
Nearest Neighbor ◽  
Selection Process ◽  
Pls Regression ◽  
K Nearest Neighbor ◽  
Wine Analysis ◽  
Data Mining Tool ◽  
Classification Technique ◽  
Mining Tool ◽  
Sensitivity Specificity ◽  
Neighbor Classification

This paper presents a method to select the best variables to categorize chemical samples into two classes, say conforming or non-conforming. For that matter, PLS regression is combined with a data mining tool, the k-Nearest Neighbor classification technique, through an iterative variable selection process. The recommended subset of variables is chosen based on several criteria: sensitivity, specificity and percent of retained variables. When applied to two datasets related to wine analysis and one associated to QSAR, the proposed method significantly reduced the number of variables required for classification, while yielding superior categorization performance when compared to using all original variables.

scholarly journals Komparasi Algoritma Nonparametrik untuk Klasifikasi Citra Wajah Berdasarkan Suku di Indonesia

2020 ◽  
Vol 6 (3) ◽  
pp. 337
Author(s):  
Seno Hartono ◽  
Anggi Perwitasari ◽  
Herry Sujaini
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Tool ◽  
Mining Tool

Klasifikasi merupakan metode data mining yang berfungsi untuk mengatur dan mengkategorikan data pada kelas yang berbeda-beda. Penelitian ini bertujuan untuk membandingkan dan menentukan algoritma nonparametrik terbaik dalam pengklasifikasian citra wajah. Dalam proses pengklasifikasian, penelitian ini menggunakan algoritma klasifikasi nonparametrik yaitu k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, dan AdaBoost Untuk mengklasifikasikan citra wajah penduduk Indonesia yang berasal dari suku Batak, Dayak, Jawa, Melayu, dan Tionghoa. Penelitian ini menggunakan Orange Data Mining Tool sebagai alat bantu untuk melakukan proses data mining. Dari hasil pengklasifikasian dengan menerapkan algoritma k-Nearest Neigbor, Support Vector Machine, Decision Tree, dan AdaBoost, SVM memberikan nilai akurasi yang lebih baik dibanding algoritma lainnya. Rata-rata nilai precision keempat algoritma tersebut berturut-turut adalah Support Vector Machine 37.5%, diikuti oleh algoritma k-Nearest Neighbor 31.55%, AdaBoost 30.25%, dan untuk Decision Tree 29.75%.

scholarly journals PENGARUH SELEKSI FITUR CITRA TERHADAP KLASIFIKASI TINGKAT KESEGARAN DAGING SAPI LOKAL

2021 ◽  
Vol 10 (1) ◽  
pp. 85
Author(s):  
Titin Yulianti ◽  
Mareli Telaumbanua ◽  
Hery Dian Septama ◽  
Helmy Fitriawan ◽  
Afri Yudamson
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Selection Process ◽  
Human Perception ◽  
Image Feature ◽  
K Nearest Neighbor ◽  
Color Intensity ◽  
Sensitivity Specificity ◽  
Selection Algorithms

Identifying beef manually has some drawbacks because human visual has limitations and there are differences of human perception in assessing object quality. Several researches developed beef quality assessment methods based on image feature extraction. However, not all features support for obtaining the classification results that have high accuracy. The efficiency will be achieved if the classification analyzes only the relevant features. Therefore, a feature selection process is required to select relevant features and to eliminate irrelevant features to obtain more accurate and faster classification results. One of the feature selection algorithms is the F-Score which is a simple technique that measures the discrimination of two sets of real numbers. The features with the lowest ranking from the F-Score will be eliminated one by one until the most relevant features are obtained. The test is carried out by analyzing the classification results in the form of sensitivity, specificity, and accuracy values. The results of this research showed that by using the F-Score feature, the most relevant features for the classification of freshness level of local beef are obtained using the K-Nearest Neighbor (KNN) method. These features include the average color intensity R and standard deviation with a sensitivity of 0.8, a specificity of 0.93, and an accuracy of 86%.  Keywords:  Classification, Fiture Selection, F-Score, K-Nearest Neighbor, Local beef

scholarly journals Detection and Classification of Caterpillar using Image Processing with K-Nearest Neighbor Classification Technique

2021 ◽  
Vol 12 (5) ◽  
pp. 719-728
Author(s):  
Dr. Mukta Jagdish, Andres Medina Guzman, Gerber F. Incacari Sancho, Aura Guerrero-Luzuriaga
Keyword(s):  
Image Processing ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Self Defense ◽  
Classification Technique ◽  
Buck Moth ◽  
Neighbor Classification ◽  
Neighbor Classifier

Caterpillars are the developmental stage of the flying insect called butterfly. The moths are the beautiful creature of earth which comes under the class of insects. They are recognized by their beautiful and colorful forewings body and legs. Caterpillars are the larval stage of the moth which are found in the leaf and stem of the plants when the moth laid eggs on the leaves after their mating. Caterpillar after fully developed from its eggs draw out a flimsy, soft cocoon made up of dark coarse silk on leaves and stem for their shelter. Caterpillar is also a beautiful creature that is found with different colors and strips with spines and urticating hair in their body for releasing venom for self-defense from external predators. The present study works on the detection and classification of the caterpillar using image processing with a k-NN classifier.This research help in characterizing the type of caterpillar image classification for particular three classes such as accuracy of Buck Moth Caterpillar, the accuracy of Saddleback Caterpillar, and the accuracy of Io moth Caterpillar. The following stages have been considered are preprocessing, segmentation, feature extraction, and classification methods using K- Nearest Neighbor classifier. The present investigation results that SYMLET5 analysis works well in the classification of the caterpillar with an accuracy of 96% using K- Nearest Neighbor classifier compare with other measures during investigation and analysis.

scholarly journals Exclusive lasso-based k-nearest-neighbor classification

2021 ◽  
Author(s):  
Lin Qiu ◽  
Yanpeng Qu ◽  
Changjing Shang ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification
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