scholarly journals The Application of the K-Nearest Neighbors Method as A Recommendation for The Selection of Departments in Higher Education Based on The Results of Multiple Intelligence Tests

2020 ◽  
Vol 1464 ◽  
pp. 012024
Author(s):  
N Nuswantari ◽  
Y F Rachman ◽  
P W D Setiawan ◽  
W D Prakoso
Keyword(s):  
Higher Education ◽  
Nearest Neighbors ◽  
Multiple Intelligence ◽  
Intelligence Tests ◽  
K Nearest Neighbors ◽  
Selection Of

scholarly journals A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 286 ◽  
Author(s):  
Hamid Saadatfar ◽  
Samiyeh Khosravi ◽  
Javad Hassannataj Joloudari ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband
Keyword(s):  
Big Data ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Parametric Classification ◽  
Efficient Data ◽  
Data Pruning ◽  
Selection Of

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.

Prediction of diameter distributions and tree-lists in southwestern Oregon using LiDAR and stand-level auxiliary information

2019 ◽  
Vol 49 (7) ◽  
pp. 775-787 ◽  
Author(s):  
Francisco Mauro ◽  
Bryce Frank ◽  
Vicente J. Monleon ◽  
Hailemariam Temesgen ◽  
Kevin R. Ford
Keyword(s):  
Auxiliary Information ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Auxiliary Data ◽  
Species Groups ◽  
Definition Of ◽  
Evaluation Of Methods ◽  
Forest Managers ◽  
Selection Of ◽  
Diameter Distributions

Diameter distributions and tree-lists provide information about forest stocks disaggregated by size and species and are key for informing forest management. Diameter distributions and tree-lists are multivariate responses, which makes the evaluation of methods for their prediction reliant on the use of dissimilarity metrics to summarize differences between observations and predictions. We compared four strategies for selection of k nearest neighbors (k-NN) methods to predict diameter distributions and tree-lists using LiDAR and stand-level auxiliary data and analyzed the effect of the k-NN distance and number of neighbors in the performance of the predictions. Strategies differed by the dissimilarity metric used to search for optimal k-NN configurations and the presence or absence of post-stratification. We also analyzed how alternative k-NN configurations ranked when tree-lists were aggregated using different DBH classes and species groupings. For all dissimilarity metrics, k-NN configurations using random-forest distance and three or more neighbors provided the best results. Rankings of k-NN configurations based on different dissimilarity metrics were relatively insensitive to changes on the width of the DBH classes and the definition of the species groups. The selection of the k-NN methods was clearly dependent on the choice of the dissimilarity metric. Further research is needed to find suitable ways to define dissimilarity metrics that reflect how forest managers evaluate differences between predicted and observed tree-lists and diameter distributions.

scholarly journals Finding The Most Desirable Car Using K-Nearest Neighbor From E-Commerce Websites

2021 ◽  
Vol 5 (1) ◽  
pp. 25-31
Author(s):  
Mohammad Farid Naufal ◽  
Yudistira Rahadian Wibisono
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Fuel Type ◽  
K Nearest Neighbor ◽  
Minkowski Distance ◽  
Search System ◽  
K Nearest Neighbors ◽  
Automatic Data ◽  
Selection Of

The increasing number of cars that have been released to the market makes it more difficult for buyer to choose the choice of car that fits with their desired criteria such as transmission, number of kilometers, fuel type, and the year the car was made. The method that is suitable in determining the criteria desired by the community is the K-Nearest Neighbors (KNN). This method is used to find the lowest distance from each data in a car with the criteria desired by the buyer. Euclidean, Manhattan, and Minkowski distance are used for measuring the distance. For supporting the selection of cars, we need an automatic data col-lection method by using web crawling in which the system can retrieve car data from several ecommerce websites. With the construction of the car search system, the system can help the buyer in choosing a car and Euclidean distance has the best accuracy of 94.40%.

scholarly journals A Novel Density Peaks Clustering Algorithm Based on K Nearest Neighbors With Adaptive Merging Strategy

2020 ◽  
Author(s):  
Xiaoning Yuan ◽  
Hang Yu ◽  
Jun Liang ◽  
Bing Xu
Keyword(s):  
Real World ◽  
Clustering Algorithm ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Cutoff Distance ◽  
Real World Datasets ◽  
Merging Strategy ◽  
Selection Of

Abstract Recently the density peaks clustering algorithm (dubbed as DPC) attracts lots of attention. The DPC is able to quickly find cluster centers and complete clustering tasks. And the DPC is suitable for many clustering tasks. However, the cutoff distance 𝑑𝑑𝑐𝑐 is depends on human experience which will greatly affect the clustering results. In addition, the selection of cluster centers requires manual participation which will affect the clustering efficiency. In order to solve these problem, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (dubbed as KNN-ADPC). We propose a clusters merging strategy to automatically aggregate the over-segmented clusters. Additionally, the K nearest neighbors is adopted to divide points more reasonably. The KNN-ADPC only has one parameter and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove the higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC and DPC-KNN.

scholarly journals Mapping the Growing Stem Volume of the Coniferous Plantations in North China Using Multispectral Data from Integrated GF-2 and Sentinel-2 Images and an Optimized Feature Variable Selection Method

2021 ◽  
Vol 13 (14) ◽  
pp. 2740
Author(s):  
Xinyu Li ◽  
Hui Lin ◽  
Jiangping Long ◽  
Xiaodong Xu
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Nearest Neighbors ◽  
Stem Volume ◽  
K Nearest Neighbors ◽  
Combination Optimization ◽  
Variable Combination ◽  
Coniferous Plantations ◽  
Selection Of ◽  
Sentinel 2

Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global scales as it is easily accessible, has a wide coverage, and mature technology. However, their application is limited by cloud coverage, data stripes, atmospheric effects, and satellite sensor errors. Combining multi-sensor data can reduce such limitations as it increases the data availability, but also causes the multi-dimensional problem that increases the difficulty of feature selection. In this study, GaoFen-2 (GF-2) and Sentinel-2 images were integrated, and feature variables and data scenarios were derived by a proposed adaptive feature variable combination optimization (AFCO) program for estimating the GSV of coniferous plantations. The AFCO algorithm was compared to four traditional feature variable selection methods, namely, random forest (RF), stepwise random forest (SRF), fast iterative feature selection method for k-nearest neighbors (KNN-FIFS), and the feature variable screening and combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (DC-FSCK). The comparison indicated that the AFCO program not only considered the combination effect of feature variables, but also optimized the selection of the first feature variable, error threshold, and selection of the estimation model. Furthermore, we selected feature variables from three datasets (GF-2, Sentinel-2, and the integrated data) following the AFCO and four other feature selection methods and used the k-nearest neighbors (KNN) and random forest regression (RFR) to estimate the GSV of coniferous plantations in northern China. The results indicated that the integrated data improved the GSV estimation accuracy of coniferous plantations, with relative root mean square errors (RMSErs) of 15.0% and 19.6%, which were lower than those of GF-2 and Sentinel-2 data, respectively. In particular, the texture feature variables derived from GF-2 red band image have a significant impact on GSV estimation performance of the integrated dataset. For most data scenarios, the AFCO algorithm gained more accurate GSV estimates, as the RMSErs were 30.0%, 23.7%, 17.7%, and 17.5% lower than those of RF, SRF, KNN-FIFS, and DC-FSCK, respectively. The GSV distribution map obtained by the AFCO method and RFR model matched the field observations well. This study provides some insight into the application of optical images, optimization of the feature variable combination, and modeling algorithm selection for estimating the GSV of coniferous plantations.

scholarly journals A novel density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy

2021 ◽  
Author(s):  
Xiaoning Yuan ◽  
Hang Yu ◽  
Jun Liang ◽  
Bing Xu
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Density Peaks ◽  
Data Points ◽  
Density Peaks Clustering ◽  
Cutoff Distance ◽  
Real World Datasets ◽  
Merging Strategy ◽  
Selection Of

AbstractRecently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for different kinds of clustering tasks. However, deciding the cutoff distance $${d}_{c}$$ d c largely depends on human experience which greatly affects clustering results. In addition, the selection of cluster centers requires manual participation which affects the efficiency of the algorithm. In order to solve these problems, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (KNN-ADPC). A clusters merging strategy is proposed to automatically aggregate over-segmented clusters. Additionally, the K nearest neighbors are adopted to divide data points more reasonably. There is only one parameter in KNN-ADPC algorithm, and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC, and DPC-KNN.

Identify This…

2009 ◽  
Vol 2 (2) ◽  
pp. 209-228 ◽  
Author(s):  
Leslie Rebecca Bloom ◽  
Amanda Reynolds ◽  
Rosemary Amore ◽  
Angela Beaman ◽  
Gatenipa Kate Chantem ◽  
...  
Keyword(s):  
Higher Education ◽  
Life History ◽  
Graduate Students ◽  
Women's Studies ◽  
Essential Elements ◽  
Women's Voices ◽  
Readers Theater ◽  
Creative Pedagogy ◽  
And Gender ◽  
Selection Of

Readers theater productions are meaningful expressions of creative pedagogy in higher education. This article presents the script of a readers theater called Identify This… A Readers Theater of Women's Voices, which was researched, written, and produced by undergraduate and graduate students in a women's studies class called Intersections of Race, Class, and Gender. Section one of the article reproduces the script of Identify This that was based on life history interviews with a diverse selection of women to illustrate intersectional identities. Section two briefly describes the essential elements of the process we used to create and perform Identify This.

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