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A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

Overview of attention for article published in PLOS ONE, November 2014
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Title
A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network
Published in
PLOS ONE, November 2014
DOI 10.1371/journal.pone.0112987
Pubmed ID
Authors

Nader Salari, Shamarina Shohaimi, Farid Najafi, Meenakshii Nallappan, Isthrinayagy Karishnarajah

Abstract

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
Unknown 82 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 20%
Student > Master 14 17%
Researcher 5 6%
Professor 3 4%
Student > Bachelor 3 4%
Other 12 14%
Unknown 29 35%
Readers by discipline Count As %
Computer Science 25 30%
Medicine and Dentistry 7 8%
Engineering 4 5%
Nursing and Health Professions 3 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 8 10%
Unknown 34 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 July 2015.
All research outputs
#15,312,760
of 22,774,233 outputs
Outputs from PLOS ONE
#130,556
of 194,339 outputs
Outputs of similar age
#214,277
of 361,967 outputs
Outputs of similar age from PLOS ONE
#2,634
of 4,488 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,339 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 361,967 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,488 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.