Improving KNN by Gases Brownian Motion Optimization Algorithm to Breast Cancer Detection

Data Science: Journal of Computing and Applied Informatics

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Field Value
 
Title Improving KNN by Gases Brownian Motion Optimization Algorithm to Breast Cancer Detection
 
Creator Abdolrazzagh-Nezhad, Majid
Shokooh Pour Mahyabadi
Ali Ebrahimpoor
 
Subject Breast Cancer Detection
Classification
K-Nearest-Neighbor Algorithm
Gases Brownian Motion Optimization
 
Description In the last decade, the application of information technology and artificial intelligence algorithms are widely developed in collecting information of cancer patients and detecting them based on proposing various detection algorithms. The K-Nearest-Neighbor classification algorithm (KNN) is one of the most popular of detection algorithms, which has two challenges in determining the value of k and the volume of computations proportional to the size of the data and sample selected for training. In this paper, the Gaussian Brownian Motion Optimization (GBMO) algorithm is utilized for improving the KNN performance to breast cancer detection. To achieve to this aim, each gas molecule contains the information such as a selected subset of features to apply the KNN and k value. The GBMO has lower time-complexity order than other algorithms and has also been observed to perform better than other optimization algorithms in other applications. The algorithm and three well-known meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) have been implemented on five benchmark functions and compared the obtained results. The GBMO+KNN performed on three benchmark datasets of breast cancer from UCI and the obtained results are compared with other existing cancer detection algorithms. These comparisons show significantly improves this classification accuracy with the proposed detection algorithm.
 
Publisher Talenta Publisher
 
Date 2020-02-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://talenta.usu.ac.id/JoCAI/article/view/3619
10.32734/jocai.v4.i1-3619
 
Source Data Science: Journal of Computing and Applied Informatics; Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI); 1-15
2580-829X
2580-6769
 
Language eng
 
Relation https://talenta.usu.ac.id/JoCAI/article/view/3619/2718
 
Rights Copyright (c) 2020 Data Science: Journal of Computing and Applied Informatics
https://creativecommons.org/licenses/by-nc-nd/4.0
 

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