scholarly journals A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator

2020 ◽  
Vol 8 (4) ◽  
pp. 276-283
Author(s):  
Ahmad Taufiq Akbar ◽  
Rochmat Husaini ◽  
Bagus Muhammad Akbar ◽  
Shoffan Saifullah
Keyword(s):  
Nearest Neighbor ◽  
Data Use ◽  
Blood Type ◽  
K Nearest Neighbor ◽  
Myers Briggs Type Indicator ◽  
Imbalance Data ◽  
Type Indicator ◽  
Testing Data ◽  
Neighbor Classification

Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.

scholarly journals Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-119
Author(s):  
Daurat Sinaga ◽  
Feri Agustina ◽  
Noor Ageng Setiyanto ◽  
Suprayogi Suprayogi ◽  
Cahaya Jatmoko
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Correlation Energy ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

scholarly journals A k-nearest neighbor classification of hERG K+ channel blockers

2016 ◽  
Vol 30 (3) ◽  
pp. 229-236 ◽  
Author(s):  
Swapnil Chavan ◽  
Ahmed Abdelaziz ◽  
Jesper G. Wiklander ◽  
Ian A. Nicholls
Keyword(s):  
Nearest Neighbor ◽  
K Channel ◽  
Channel Blockers ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

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 K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

2020 ◽  
Vol 5 (1) ◽  
pp. 23
Author(s):  
Ahmad Wali Satria Bahari Johan ◽  
Fitri Utaminingrum ◽  
Agung Setia Budi
Keyword(s):  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Class Label ◽  
K Value ◽  
Testing Data ◽  
Occurrence Matrix ◽  
Value Effect ◽  
Neighbor Classification ◽  
Smart Wheelchair

This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair

Classification of Plume Image and Analysis of Welding Stability during High Power Disc Laser Welding

2012 ◽  
Vol 201-202 ◽  
pp. 1139-1142 ◽  
Author(s):  
Qian Wen ◽  
Xiang Dong Gao
Keyword(s):  
Laser Welding ◽  
High Power ◽  
Nearest Neighbor ◽  
Welding Process ◽  
Welding Quality ◽  
K Nearest Neighbor ◽  
Dimension Vector ◽  
Absolute Value ◽  
Neighbor Classification

Plume images which captured during the high power disc laser welding contain lots of information that related to the welding quality and stability. The classification of plume images is an important foundation for online monitoring during welding process. Stainless steel 304 was taken as the experiment object for the high power disc laser welding experiments. A high-speed camera was used to capture the ultraviolet band and visible light band plume images in the laser welding process. Image processing techniques were applied to get the characteristic parameters such as the ratio of the absolute value of coordinate difference between the centriod of plume and the welding point, the number of spatter, the average gray level and entropy of a spatter image, and formed a four dimension vector. Then K-nearest neighbor classification method was used to separate the poor welding quality images out from good ones. Welding experimental results confirmed that using K- nearest neighbor classification method to classify the four dimension vector samples which formed by the ratio of absolute value of coordinate difference between the centriod of plume and welding point, number of spatters, average gray level and entropy could obtain a recognition rate that close to the actual welding results.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Sign in / Sign up

Export Citation Format

Share Document