Particle Swarm based Data Mining Algorithms for classification tasks

By Tiago Ferra de Sousa, Arlindo Silva, Ana Neves. Parallel Computing, 2004

356 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/86a5773064873ff5b710af5ed4ce63436c3b622c

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Citations in the corpus

Backtracking Search Optimization Algorithm for numerical optimization problems (2013, methodology): “EAs have been used for mechanical design problems [14], communication applications [15], image processing applications [16], speech recognition problems [17], sensor deployment problems [18], data mining applications [19], IIR filter design [20], solar radiation modeling [21], lens design [22], dyna”
A review of population-based meta-heuristic algorithm (2013, background): “…and their improved scheme have shown a good performance in the wide range of problems such as neural network training [17, 18], pattern recognition [19, 20], function optimization [21, 22], image processing [23, 24], data mining [25, 26], combinatorial optimization problems [27, 28] and so on.”
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds (2014, methodology): “Many problems in different areas have been successfully addressed by adapting PSO to specific domains; for instance this technique has been used to solve problems in areas such as reactive voltage control [5], pattern recognition [6] and data mining [7], among others.”
A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments (2010, methodology): “Some of the applications that have used PSO are: the reactive voltage control problem [23], data mining [16], chemical engineering [13], pattern recognition [9] and environmental engineering [10].”
A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis (2014, methodology): “The purpose of the validation algorithm is to statistically evaluate the accuracy of the results (Sousa et al., 2004).”