Thalassaemia classification by neural networks and genetic programming

Wongseree, Waranyu; Chaiyaratana, Nachol; Vichittumaros, Kanjana; Winichagoon, Pranee; Fucharoen, Suthat

doi:10.1016/j.ins.2006.07.009

Cited by 55 publications

(37 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In crossover operation, randomly selected sub-trees of two parent trees were swapped. The swapping includes the exchange of activation-nodes, weights, and inputs as it is described in [38,64,68].…”

Section: Tree-constructionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

View full text Add to dashboard Cite

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features.MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.

show abstract

Section: Tree-constructionmentioning

confidence: 99%

“…The mutation of a selected individual from mating pool took place in the following manner [38,64,68]: 1) A randomly selected terminal node is replaced by a newly generated terminal node.…”

Section: Tree-constructionmentioning

confidence: 99%

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…GP has been applied in various branches of engineering and sciences ranging from neural networks [5,34,50], image processing and pattern recognition [8,27,33,36,47], biomedical science [18,22,39,55,56,21,26,51] to control engineering [28,30,45] and robotics [6,23,40,44,46,58,41]. It has also been used in various applications such as for search engine ranking function optimization by Fan et al [12][13][14][15][16][17], classification tasks [54,60,32] and navigation tasks [4], to name but a few. Like other approaches in evolutionary computation, GP is normally computer processing intensive and therefore special methods are necessary in order to increase or enhance its performance.…”

Section: Introductionmentioning

confidence: 99%

Population variation in genetic programming

Kouchakpour

Zaknich

Bräunl

2007

Information Sciences

View full text Add to dashboard Cite

“…In the last few years, GP has been extensively used both in Industry and Academia (Arcuri & Yao, 2010;Chan, Kwong, & Fogarty, 2010;Choi & Choi, 2012;dos Santos, Ferreira, Torres, Gonçalves, & Lamparelli, 2011;Koza, Streeter, & Keane, 2008;Moreno-Torres, Llorá, Goldberg, & Bhargava, 2013;Ravisankar, Ravi, & Bose, 2010;Trujillo, Legrand, Olague, & Lévy-Véhel, 2012;Yeun, Suh, & Yang, 2000;Wongseree, Chaiyaratana, Vichittumaros, Winichagoon, & Fucharoen, 2007) and it has produced a wide set of results that have been defined human-competitive (Koza, 2010). While these results have demonstrated the appropriateness of GP in tackling real-life problems, research has recently focused on developing new variants of GP in order to further improve its performance.…”

Section: Geometric Semantic Operatorsmentioning

confidence: 99%