The welfare classification of Indonesian National Civil servant using TOPSIS and k-Nearest Neighbour (KNN)

2016 ◽  
Author(s):  
Wina Permana Sari ◽  
Elin Cahyaningsih ◽  
Dana Indra Sensuse ◽  
Handrie Noprisson
Keyword(s):  
Civil Servant ◽  
Nearest Neighbour

Automated System Design for Classification of Chronic Lung Viruses using Non-Linear Dynamic System Features and K-Nearest Neighbour

2021 ◽  
Author(s):  
Misha Urooj Khan ◽  
Ayesha Farman ◽  
Asad Ur Rehman ◽  
Nida Israr ◽  
Muhammad Zulqarnain Haider Ali ◽  
...  
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Linear Dynamic System ◽  
Automated System ◽  
Nearest Neighbour ◽  
Linear Dynamic ◽  
Non Linear

scholarly journals An Improved Dual Clustering Method for Classification of Microarray Image Segementation

2019 ◽  
Vol 8 (4) ◽  
pp. 2514-2519
Keyword(s):  
Support Vector Machine ◽  
Ct Scanning ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Resonance Imaging ◽  
Computerised Tomography ◽  
Medical Field ◽  
Microarray Image ◽  
Image Segementation

Microarray is a fast and rapid growing technology which plays dynamic role in the medical field. It is an advanced than MRI (Magnetic Resonance Imaging) and CT scanning (Computerised Tomography). The purpose of this work is to make fine perfection against the gene expression. In this study the two clustering are used which fuzzy c means and k means and also it classifies with better results. The microarray data base indicates the classification in support vector machine. Segmentation is most important step in microarray image. The classification in support vector machine is compared with other two classifiers which means the k nearest neighbour and with the Bayes classifiers.

Setting Attribute Weights for Nearest Neighbor Learning Algorithms Using C4.5

1997 ◽  
Vol 11 (03) ◽  
pp. 405-415 ◽  
Author(s):  
Charles X. Ling ◽  
John J. Parry ◽  
Handong Wang
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Simple Approach ◽  
Nearest Neighbour ◽  
Attribute Weights ◽  
New Methods

Nearest Neighbour (NN) learning algorithms utilize a distance function to determine the classification of testing examples. The attribute weights in the distance function should be set appropriately. We study situations where a simple approach of setting attribute weights using decision trees does not work well, and design three improvements. We test these new methods thoroughly using artificially generated datasets and datasets from the machine learning repository.

Adequacy of a Single Visit for Classification of Hypertensive Status in a Nigerian Civil Servant Population

1994 ◽  
Vol 23 (4) ◽  
pp. 723-729 ◽  
Author(s):  
NINA MARKOVIC ◽  
ISOKEN N OLOMU ◽  
CLAREANN H BUNKER ◽  
SARA L HUSTON ◽  
FLORA A M UKOLI ◽  
...  
Keyword(s):  
Civil Servant ◽  
Single Visit

A comparison of eight numerical procedures applied to the classification of some African Trifolium taxa based on Rhizobium affinities

1967 ◽  
Vol 15 (3) ◽  
pp. 521 ◽  
Author(s):  
Mannetje L 't
Keyword(s):  
Numerical Methods ◽  
Comparative Study ◽  
Evolutionary Theory ◽  
Correlation Coefficients ◽  
Point Of View ◽  
Nearest Neighbour ◽  
Taxonomic Distance ◽  
Affinity Groups

A comparative study was made of correlation coefficients, taxonomic distance, non-metric coefficient, and probabilistic index in combmatlon with nearest neighbour, furthest neighbour, centroid, and flexible sorting as methods for the classification of 16 Trifolium taxa. The data used were published Rhizobium affinities that were shown to be meaningful from the point of view of morphology, cytology, and evolutionary theory. The criterion used in testing the numerical methods was the ability to produce groups of taxa in accordance with Rhizobium affinity groups. On the basis of this study acceptable classifications were obtained by using the non-metric coefficient with flexible sorting, taxonomic distance with furthest neighbour, and taxonomic distance with flexible sorting.

Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN, and Random Forest Learning Techniques

2018 ◽  
pp. 102-119 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Pulkit Kulshrestha ◽  
Pijush Samui
Keyword(s):  
Random Forest ◽  
Performance Metrics ◽  
Deep Belief Network ◽  
Machine Learning Algorithms ◽  
Nearest Neighbour ◽  
Data Set ◽  
Belief Network ◽  
Learning Techniques ◽  
Severe Shortage

Drought is a condition of land in which the ground water faces a severe shortage. This condition affects the survival of plants and animals. Drought can impact ecosystem and agricultural productivity, severely. Hence, the economy also gets affected by this situation. This paper proposes Deep Belief Network (DBN) learning technique, which is one of the state of the art machine learning algorithms. This proposed work uses DBN, for classification of drought and non-drought images. Also, k nearest neighbour (kNN) and random forest learning methods have been proposed for the classification of the same drought images. The performance of the Deep Belief Network(DBN) has been compared with k nearest neighbour (kNN) and random forest. The data set has been split into 80:20, 70:30 and 60:40 as train and test. Finally, the effectiveness of the three proposed models have been measured by various performance metrics.

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