ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

Alshamlan, Hala; Badr, Ghada; Al-Ohali, Yousef

doi:10.18178/ijmlc.2016.6.3.596

Cited by 55 publications

(26 citation statements)

References 18 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, 10 different classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Ten classification algorithms used in this paper are SVM (Vadali et al, 2019), Quadratic Discriminant Analysis (QDA) (Siqueira et al, 2017), CART (Cheng et al, 2017), Linear Discriminant Analysis (LDA) (Liu et al, 2016), KNN (Suyundikov et al, 2015), Ensemble (Xiao et al, 2018), ELM (Sachnev et al, 2015), Particle Swarm Optimization-Support Vector Machine (PSO-SVM) (Jiang et al, 2010), Genetic Algorithm-Support Vector Machine (GA-SVM) (Tao et al, 2019), and ABC-SVM (Alshamlan et al, 2016). Thirteen and twenty-one indicators are used as input characteristics, respectively.…”

Section: Correlation Indicators Validation and Escc Differentiation Pmentioning

confidence: 99%

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

et al. 2020

View full text Add to dashboard Cite

The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as output. Ten classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Artificial bee colony-support vector machine (ABC-SVM) predicts better than the other nine algorithms, with an average accuracy of 81.5% for the 10-fold cross-validation. Based on logistic regression and ReliefF algorithm, five models with the greater merit for the degree of differentiation are found in the development sets. The AUC values of the five models are 0.672, 0.628, 0.630, 0.628, and 0.608 (P < 0.05). The AUC values of the five models in the validation sets are 0.753, 0.728, 0.744, 0.776, and 0.868 (P < 0.0001). The predicted values of the five models are constructed as the input features of ABC-SVM. The accuracy of the 10-fold cross-validation reached 82.0 and 86.5% in the development sets and the validation sets, respectively.

show abstract

Section: Correlation Indicators Validation and Escc Differentiation Pmentioning

confidence: 99%

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Thus, selecting the most appropriate wrapper and filter seems to be challenging. Further, it seems the Evolutionary Algorithms (EA) [19][20][21][22][23][24][25][26][27] such a Genetic Algorithm (GA) [19,20], Particle Swam Optimization Algorithm (PSO) [19,23], Ant Colony Optimization Algorithm (ACO) [22] and Artificial Bee Colony Algorithm (ABC) [24] have been successfully applied in many researches in gene subset selection. Alba [19] proposed a wrapper approach with EA and SVM namely PSO-SVM and GA-SVM.…”

Section: Related Workmentioning

confidence: 99%

“…The approach was evaluated on six microarray datasets out of which five provided 100% accuracy with only few genes. The author compares the results with ABC-SVM [25], minimum Redundancy Maximum Relevance-ABC (mRMR-ABC) [26], Co-GA and Co-PSO algorithms. The results obtained using Co-ABC leads all the algorithms which were compared.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

Fajila¹,

Jahan²

2018

IJMECS

View full text Add to dashboard Cite

The fact that reflects the cancer research consequences shows that still there are improvements that should be investigated in the stream of cancer in future. This leads the researchers to actively involve further in cancer research field. As an invention, a hybrid machine learning method is proposed in this study where two filters are assessed along with a wrapper approach. Typically, filters prioritize the features while, wrappers contribute in subset identification. Though both filters and wrappers exist independently, the excellent results they produce when applied subsequently. The wrapperfilter combination plays a major role in feature selection. Yet, incorporating with a best strategy for feature space analysis is crucial in this concern. Thus, we introduce the Evolutionary Algorithm in the proposed study to search through the feature space for informative gene subset selection. Though there are several gene selection approaches for cancer classification, many of them suffer from law classification accuracy and huge gene subset for prediction. Hence, we propose Evolutionary Algorithm to overcome this problem. The proposed approach is evaluated on five microarray datasets, where three out of them provide 100% accuracy. Regardless the number of genes selected, both filters provide the same performance throughout the datasets used. As a consequence, the Evolutionary Algorithm in feature space search is highlighted for its performance in gene subset selection.

show abstract

“…Currently, a series of optimization algorithms is used to optimize the parameters of SVM (e.g. genetic algorithm (Huerta, Duval, & Hao, 2006), particle swarm optimization algorithm (Huang & Dun, 2008), artificial bee colony algorithm (Alshamlan, Badr, & Alohali, 2016)). It avoids the empirical allocation of penalty parameters to SVM, and the choice of kernel function improves the accuracy of pipeline leak condition identification.…”

Section: Introductionmentioning

confidence: 99%

A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection

Wang

Zhang

Song

et al. 2019

Systems Science & Control Engineering

View full text Add to dashboard Cite

In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of the traditional support vector machine (SVM) approaches with applications in analyzing data of oil pipeline leak detection. In the proposed saturated and mix-delayed particle swarm optimization (SMDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, time-varying time-delays and distributed time-delays are introduced in the velocity updating model to respectively reflect the history of previous personal and global optimum particles. The introduction of saturation constraint ensures that the particles will convergence in case that the velocity of the particles is too large. Eight well-known benchmark functions are employed to evaluate the proposed SMDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the developed framework SMDPSO-based SVM algorithm is exploited in the problem of oil pipeline leak detection. Experiment results demonstrate that the SMDPSO-based SVM method is superior over other well-known classification algorithms.

show abstract

ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

Cited by 55 publications

References 18 publications

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection

Contact Info

Product

Resources

About