The correlation-based redundancy multiple-filter approach for gene selection

Sharifai, Abdulrauf Garba; Zainol, Zurinahni

doi:10.1504/ijdmb.2020.105437

Cited by 4 publications

(2 citation statements)

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“…is met, the number of halted generations, or the best fitness value is obtained. Algorithm 1: The main steps of the GOA algorithm Initialize a set of random solutions xi (i = 1,2,3, … , n) as an initial population Initialize the GOA parameters cMax, cMin, and Max number of iterations Evaluate the fitness of all individuals T = the best solution while (k < maximum number of iterations) do Update c using equation (7) for each solution in the population, do Standardize the distance between grasshopper into [1,4] Update the position vectors using equation (8) Update the step vectors according to equation ( 10) end for there is a better solution, update T k = k+1 end while Output the T Algorithm 1 presents the pseudo-code of the conventional GOA algorithm. It can be observed that the GOA algorithm randomly generates its initial population and assesses each search agent using an objective criterion upon the optimization process begin.…”

Section: Termination Phasementioning

confidence: 99%

“…Over the last decades, rapid technological developments have enabled researchers to analyse a massive amount of data from various application domains such as biomedical, information retrieval, and text classification [1]. The characteristics of these datasets are a massive number of features with limited available samples and imbalanced class distribution; these open challenges have degraded the classification performance of most learning algorithms [2].…”

Section: Introductionmentioning

confidence: 99%

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Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

Sharifai

Zainol

2021

IEEE Access

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DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

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