A multi-objective heuristic algorithm for gene expression microarray data classification

Jia, Lixin; Peng, Qinke; Chen, Xiao; Sun, Zhoutong

doi:10.1016/j.eswa.2016.04.020

Cited by 59 publications

(17 citation statements)

References 42 publications

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“…Moreover, there is a large volume of published studies introducing new feature selection algorithms for solving various problems. For instanace, a multi objective heuristic algorithm based on analytic hierarchy process has been introduced in clinical medicine to find a discriminatory subset of genes that help diagnose and treat cancer [28]. Another study adopts the existing algorithms to select significant features from combined multi scale and different origin data and use them in biotechnological applications such as strain selection in winemaking [29].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral selection based algorithm for plant classification

AlSuwaidi

Veys

Hussey

et al. 2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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Abstract-The popularity of using hyperspectral imaging systems in studying and monitoring plant properties and conditions has increased lately. This increase has been driven by both financial and environmental advantages of such systems. Using a nondestructive hyperspectral imaging system improves the breeding process, increases profit, and reduces the usage of herbicide, thus reducing side effects to plants and environment. This paper is concerned with the use of hyperspectral image analysis for differentiating different plant species as well as their conditions. The main contribution of the work lies in the use of feature selection for choosing relevant, discriminant spectral information as the input to the classifier (e.g. SVM), as compared to the use of empirical spectral indices. Two independent hyperspectral datasets, captured by different instrumentations, were used in evaluation. Experimental results show significant improvements in classification accuracy with several feature selection algorithms compared to with the spectral vegetation and disease indices. The study shows that systematically selection of wavelength features can shed light on attributes that differentiate plants and their conditions.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral selection based algorithm for plant classification

AlSuwaidi

Veys

Hussey

et al. 2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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“…(1) cg14918082, (2) cg27210390, (3) cg01993576, (4) cg19686152, (5) cg19761273, (6) cg13870494, (7) cg19945840, (8) cg09427311, (9) cg17791651, (10) cg06058597, (11) cg10591174, (12) cg23591869, (13) cg21545849, (14) cg15368822, (15) cg20544605, (16) cg03473518, (17) cg09626984, (18) cg03375002, (19) cg00831028, (20) cg08351331, (1) cg14918082, (2) cg27210390, (3) cg01993576, (4) cg19686152, (5) cg19761273, (6) cg13870494, (7) cg19945840, (8) cg09427311, (9) cg17791651, (10) cg06058597, (11) cg10591174, (12) cg23591869, (13) cg21545849, (14) cg15368822, (15) cg20544605, (16) cg03473518, (17) cg09626984, (18) cg03375002, (19) cg00831028, (20) cg08351331, (21) cg16786458, (22) cg19180828.…”

Section: Age Rangementioning

confidence: 99%

“…There are several solutions for improving execution time of the complex wrapper methods for analyzing genomic data. In a series of studies, wrapper methods are used in combination with fast filter methodes [15][16][17][18]. In this approach, first, the dimensionality of the feature space is reduced from several thousand to several tens of features by taking advantage of a filter feature selection algorithm, and then a wrapper feature selection method is used to choose features from the remaining ones.…”

Section: Introductionmentioning

confidence: 99%

MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Momeni

Abadeh

2019

Genes

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Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R2) of 95.96% between age and DNAm. In the train data, the MAD and R2 are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

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“…AUC Because the predictive accuracy is not a suitable measure for imbalanced data, this study evaluates 20 imbalanced datasets using AUC (area under the ROC curve) [68,69]. The AUC is wildly used for classification evaluation for imbalanced data [70,71], which measure the diagnostic accuracy of a test. The AUC value lies between 0 and 1, the higher value is the better average accuracy test.…”

Section: The Performance Measuresmentioning

confidence: 99%

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Sriwanna

Boongoen

Iam-On

2017

Hum. Cent. Comput. Inf. Sci.

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Background Discretization is a data reduction preprocessing technique in data mining. It transforms a numeric or continuous attribute to a nominal or categorical attribute by replacing the raw values of a continuous attribute with non-overlapping interval labels (e.g., 0-5, 6-10, etc.). Different data mining algorithms are designed to handle different data types. Some are designed to handle only either numerical data or nominal data, while some can cope with both. Because real datasets are always a combination of numeric and nominal vales, for an algorithm that only takes nominal data, numerical attributes need to be discretized into nominal attributes before the learning algorithm. After discretization, the subsequence mining process may be more efficient as the data is reduced and simplified resulting in more noticeable patterns [1-3]. Moreover, discretization is also expected to improve the predictive accuracy for classification [4] and Label Ranking [5].

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A multi-objective heuristic algorithm for gene expression microarray data classification

Cited by 59 publications

References 42 publications

Hyperspectral selection based algorithm for plant classification

Hyperspectral selection based algorithm for plant classification

MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

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