A Survey on the Application of Genetic Programming to Classification

Espejo, Pedro G.; Ventura, Sebastián; Herrera, F.

doi:10.1109/tsmcc.2009.2033566

Cited by 494 publications

(253 citation statements)

References 134 publications

Supporting

Mentioning

247

Contrasting

Unclassified

Order By: Relevance

“…Evolutionary Algorithms [19,20], and specifically, Genetic Programming [13], have been successfully employed for obtaining classification rules. However, for every generation a population of rules must be evaluated according to a fitness function.…”

Section: Rule-based Modelsmentioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

View full text Add to dashboard Cite

Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second.

show abstract

Section: Rule-based Modelsmentioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

View full text Add to dashboard Cite

show abstract

“…Evolutionary and bio-inspired algorithms have been widely used to support the construction of ensemble classifiers under both homogeneous and heterogeneous alternatives [6][7][8][9]11]. The most tackled problems from the evolutionary perspective are those of (i) selecting ensemble members and (ii) adjusting weights in a linear combination approach.…”

Section: Related Workmentioning

confidence: 99%

“…The main aim in this paper is to evolve an improved fusion function. GP has been used for ensemble learning, see [11] for a recent and comprehensive survey. Usually classifiers based on GP are used to build an ensemble [13].…”

Section: Related Workmentioning

confidence: 99%

Genetic Programming of Heterogeneous Ensembles for Classification

Acosta-Mendoza

Morales-Reyes

Gago-Alonso

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Abstract. The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used for combining classifiers outputs in both classical and evolutionary approaches. This study proposes a novel genetic program that learns a fusion function for combining heterogeneous-classifiers outputs. It evolves a population of fusion functions in order to maximize the classification accuracy. Highly non-linear functions are obtained with the proposed method, subsuming the existing weighted-sum formulations. Experimental results show the effectiveness of the proposed approach, which can be used not only with heterogeneous classifiers but also with homogeneous-classifiers and under bagging/boosting based formulations.

show abstract

“…Genetic algorithms [7,8] employ metaphor from biology and genetics to iteratively evolve a population of initial individuals to a population of high quality individuals, where each individual represents a solution of the problem to be solved and is composed of a fixed number of genes. The number of possible values of each gene is called the cardinality of the gene.…”

Section: Genetic Algorithm Optimizationmentioning

confidence: 99%

Two Multi-Objective Genetic Algorithms for Finding Optimum Design of an I-beam

Khazaee¹,

Naimi²

2011

ENG

View full text Add to dashboard Cite

Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. Genetic algorithm optimization (GAO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper applies two GAO-based approaches to multi-objective engineering design and finds design variables through the feasible space. To demonstrate the utility of the proposed methods, the multi-objective design of an I-beam will be presented.

show abstract

A Survey on the Application of Genetic Programming to Classification

Cited by 494 publications

References 134 publications

Speeding up multiple instance learning classification rules on GPUs

Speeding up multiple instance learning classification rules on GPUs

Genetic Programming of Heterogeneous Ensembles for Classification

Two Multi-Objective Genetic Algorithms for Finding Optimum Design of an I-beam

Contact Info

Product

Resources

About