Genetic programming in classifying large-scale data: an ensemble method

Zhang, Yifeng; Bhattacharyya, Siddhartha

doi:10.1016/j.ins.2003.03.028

Cited by 71 publications

(29 citation statements)

References 32 publications

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“…In the recent years, the GP algorithm which is inspired by the nature and is based on the Darwin' theory of evolution has been used for various purposes [7][8][9][10][11][12][13][14]. In 1992, John Koza used this algorithm as a specialization of the genetic algorithm (GA) with a difference that programs [15,16].…”

Section: Sf = A/hmentioning

confidence: 99%

“…Therefore, these algorithms are usually very slow and because of working on the basis of trees, more individuals in the population, more efficient and faster algorithms are obtained [14]. However, in general case, attaining a desired fitness value is very time consuming, but this is not very horrific because with one time of running the algorithm, a model attains which could be used simply for the problem.…”

Section: Sf = A/hmentioning

confidence: 99%

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Prediction of Concrete Faced Rock Fill Dams Settlements Using Genetic Programming Algorithm

Marandi¹,

VaeziNejad²,

Khavari³

2012

IJG

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In the present study a Genetic Programing model (GP) proposed for the prediction of relative crest settlement of concrete faced rock fill dams. To this end information of 30 large dams constructed in seven countries across the world is gathered with their reported settlements. The results showed that the GP model is able to estimate the dam settlement properly based on four properties, void ratio of dam's body (e), height (H), vertical deformation modulus (E v ) and shape factor (Sc) of the dam. For verification of the model applicability, obtained results compared with other research methods such as Clements' formula and the finite element model. The comparison showed that in all cases the GP model led to be more accurate than those of performed in literature. Also a proper compatibility between the GP model and the finite element model was perceived.

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Section: Sf = A/hmentioning

confidence: 99%

Section: Sf = A/hmentioning

confidence: 99%

Prediction of Concrete Faced Rock Fill Dams Settlements Using Genetic Programming Algorithm

Marandi¹,

VaeziNejad²,

Khavari³

2012

IJG

View full text Add to dashboard Cite

show abstract

“…Genetic programming was proposed by Koza [25] to automatically extract intangible relationships in a system and has been used in many applications such as symbolic regression [26], and classification [27,28]. The representation of GP can be viewed as a tree-based structure composed of the function set and terminal set.…”

Section: Genetic Programmingmentioning

confidence: 99%

Two-stage genetic programming (2SGP) for the credit scoring model

Huang

Tzeng

Ong

2006

Applied Mathematics and Computation

127

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Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF-THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models.

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“…The classification rules obtained by genetic programming have different structures and use different genes. It signifies that the parallel genetic programming might naturally generate diverse rules by selecting different sets of attributes and structures [20]. Before combining classification rules, diversity is measured by the edit distance of structures and the appearance of genes used.…”

Section: Selecting Diverse Rulesmentioning

confidence: 99%

Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

Hong

Cho

2005

Modeling Decisions for Artificial Intelligence

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Abstract. Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated. Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method. High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.

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Genetic programming in classifying large-scale data: an ensemble method

Cited by 71 publications

References 32 publications

Prediction of Concrete Faced Rock Fill Dams Settlements Using Genetic Programming Algorithm

Prediction of Concrete Faced Rock Fill Dams Settlements Using Genetic Programming Algorithm

Two-stage genetic programming (2SGP) for the credit scoring model

Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

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