scholarly journals HYBRID STOCHASTIC DIFFUSION SEARCH – TABU SEARCH ALGORITHM WITH VARIOUS CLASSIFIERS IN BIG DATA

2020 ◽  
Vol 7 (14) ◽  
Keyword(s):  
Big Data ◽  
Tabu Search ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Stochastic Diffusion ◽  
Stochastic Diffusion Search

scholarly journals Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

2020 ◽  
Vol 8 (4S5) ◽  
pp. 236-242
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Big Data Analysis ◽  
Combinatorial Problems ◽  
Support Vector ◽  
Feature Subset ◽  
Stochastic Diffusion ◽  
Stochastic Diffusion Search

Big Data analysis has been viewed as the processing or mining of massive amounts of data used to retrieve information which is useful from large datasets. Among all the methods employed to deal with the analysis of Big Data, the selection of a feature is found extremely effective. A common approach which includes search making use of feature-based subsets which is relevant to the topic, tends to represent the dataset with its actual description. However, a search that makes use of such a subset is a combinatorial problem which is time-consuming. All commonly used meta-heuristic algorithms to facilitate feature choice. The Stochastic Diffusion Search (SDS) based algorithm has been a multi-agent global search algorithm based on agent interaction is simple to overcome combinatorial problems. The SDS will choose the feature subset for the task of classification. The Classification and Regression Tree (CART), the Naïve Bayes (NB), the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN) have been used to improve the performance. Results proved that the proposed method was able to achieve a better performance than existing techniques.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

Networks ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 322-340 ◽  
Author(s):  
Richard S. Barr ◽  
Fred Glover ◽  
Toby Huskinson ◽  
Gary Kochenberger
Keyword(s):  
Tabu Search ◽  
Extreme Point ◽  
Search Algorithm ◽  
Fixed Charge ◽  
Tabu Search Algorithm ◽  
Network Problems

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

A Tabu Search algorithm for the optimisation of telecommunication networks

1998 ◽  
Vol 106 (2-3) ◽  
pp. 357-372 ◽  
Author(s):  
Eugenio Costamagna ◽  
Alessandra Fanni ◽  
Giorgio Giacinto
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Telecommunication Networks ◽  
Tabu Search Algorithm

Study on Tabu Search Algorithm for Multi-Cargo Loading Problem

2012 ◽  
Vol 178-181 ◽  
pp. 1802-1805
Author(s):  
Chun Yu Ren
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Initial Solution ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Search Efficiency ◽  
Speed Of Convergence ◽  
Neighborhood Structure ◽  
Loading Problem

The paper is focused on the Multi-cargo Loading Problem (MCLP). Tabu search algorithm is an algorithm based on neighborhood search. According to the features of the problem, the essay centered the construct initial solution to construct neighborhood structure. For the operation, 1-move and 2-opt were applied, it can also fasten the speed of convergence, and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and concrete examples.

A chaos algorithm based on progressive optimality and tabu search algorithm

2005 ◽  
Author(s):  
Jun-Jie Yang ◽  
Jiang-Zhong Zhou ◽  
Wei Wu ◽  
Fang Liu ◽  
Cheng-Jun Zhu ◽  
...  
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Chaos Algorithm
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