A memetic algorithm to select training data for support vector machines

Nalepa, Jakub; Kawulok, Michał

doi:10.1145/2576768.2598370

Cited by 39 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such techniques have been shown extremely effective in solving numerous challenging problems (Nalepa and Blocho 2016). Nalepa and Kawulok (2014b) proposed the first MA (termed MASVM) for selecting refined SVM training sets. The pool of important vectors (which were selected as SVs during the evolution) is maintained and used to educate the population, and to introduce super individuals-refined sets composed of SVs only.…”

Section: Evolutionary Methodsmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

Self Cite

View full text Add to dashboard Cite

Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora of real-life applications. However, they suffer from the important shortcomings of their high time and memory training complexities, which depend on the training set size. This issue is especially challenging nowadays, since the amount of data generated every second becomes tremendously large in many domains. This review provides an extensive survey on existing methods for selecting SVM training data from large datasets. We divide the state-of-the-art techniques into several categories. They help understand the underlying ideas behind these algorithms, which may be useful in designing new methods to deal with this important problem. The review is complemented with the discussion on the future research pathways which can make SVMs easier to exploit in practice.

show abstract

Section: Evolutionary Methodsmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this condition, a reasonable portion of the large training dataset should be selected for retraining the existing model. For this purpose, methods for data reduction in SVM classification models have been proposed [28,29], including methods based on the convex hull [27,30].…”

Section: Q4mentioning

confidence: 99%

A convex hull-based data selection method for data driven models

Khosravani

Ruano

Ferreira

2016

Applied Soft Computing

View full text Add to dashboard Cite

“…MAs (both sequential and parallel) were shown to be very effective in solving the VRPTW (Nagata et al 2010;Czech 2012a, b, 2013;Nalepa and Czech 2013;Vidal et al 2013, Nalepa and. Memetic techniques have been applied to a bunch of other optimization and pattern recognition problems in a variety of science and engineering domains (Li et al 2013(Li et al , 2014Guan et al 2014;Jin et al 2014;Nalepa and Kawulok 2014), and they outperformed other evolutionary algorithms in terms of the convergence capabilities.…”

Section: Vehicle Routing Problem With Time Windowsmentioning

confidence: 99%

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Nalepa

Błocho²

2015

Soft Comput

Self Cite

View full text Add to dashboard Cite

This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with

show abstract

A memetic algorithm to select training data for support vector machines

Cited by 39 publications

References 25 publications

Selecting training sets for support vector machines: a review

Selecting training sets for support vector machines: a review

A convex hull-based data selection method for data driven models

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Contact Info

Product

Resources

About