A Hybrid Dimensionality Reduction Model for Classification of Microarray Dataset

Arowol, Micheal O.; Abdulsalam, Sulaiman Olaniyi; Isiaka, Rafiu Mope; Gbolagade, Kazeem Alagbe

doi:10.5815/ijitcs.2017.11.06

Cited by 13 publications

(13 citation statements)

References 13 publications

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“…The classifier uses an ensemble classification learning evaluation procedure, the training and testing segments use 10-fold cross-validation for eliminating selection partialities using MATLAB. Evaluation outcome is constructed using the computational time and performance metrics [27]-classification performance with Ada-Boost and Bagging Ensemble classification models, with 93.3% and 95% accuracy respectively. The result procedures are shown in Figures Figure 2.…”

Section: Resultsmentioning

confidence: 99%

“…Addressing the importance of features, GA is used in finding the ideal feature subset by means of the nominated figure of features for complex classification presentation. The general construction of the GA is defined in Algorithm 1 below by adopting [27] m is the population size, r is a random number lying flanked by 0 to 1, signifies the nominated chrome or unselected feature with a threshold δ set value to be 0.5, and α is the threshold number of features nominated. The significant problems of the precise method are selecting the maximum fitting features from the predictable datasets.…”

Section: Methodsmentioning

confidence: 99%

“…Performance evaluation of machine learning technique entails validation metrics such as a confusion matrix, used for analyzing classification models features, discovering the classified illustrations from the given model of tested dataset model samples [5] using the performance metrics formulas [22,27].…”

Section: Performance Evaluationmentioning

confidence: 99%

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Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

Arowolo

Adebiyi

et al. 2021

IJEECS

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<p>Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.</p>

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

Arowolo

Adebiyi

et al. 2021

IJEECS

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“…The hybrid approach combines the filter and wrapper technique and seeks to incorporate the filter and wrapper methods. Ultimately, the embedded techniques take advantage of the selection of features in the learning process as well as are highly comparable to a certain learning model [45,46].…”

Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 0.52% higher than the PSO, 1.20% higher than ACO, and 1.57 higher than the ABC algorithm.

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“…Micheal O. Arowolo et al explored a hybrid dimensionality reduction approach to classify the microarray data. Moreover, the performance evaluation metrics such as time taken for training, accuracy, sensitivity, specificity, precision, area under curve and error are used to justify the performance of the proposed system [14].…”

Section: Related Workmentioning

confidence: 99%

Dimensionality Reduction for Classification and Clustering

Singh¹,

Leavline²

2019

IJISA

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Now-a-days, data are generated massively from various sectors such as medical, educational, commercial, etc. Processing these data is a challenging task since the massive data take more time to process and make decision. Therefore, reducing the size of data for processing is a pressing need. The size of the data can be reduced using dimensionality reduction methods. The dimensionality reduction is known as feature selection or variable selection. The dimensionality reduction reduces the number of features present in the dataset by removing the irrelevant and redundant variables to improve the accuracy of the classification and clustering tasks. The classification and clustering techniques play a significant role in decision making. Improving accuracy of classification and clustering is an essential task of the researchers to improve the quality of decision making. Therefore, this paper presents a dimensionality reduction method with wrapper approach to improve the accuracy of classification and clustering. Index Terms-Wrapper-based dimensionality reduction, naï ve Bayes classifier, Random forest classifier, OneR classifier, Variable selection. Resource Management, and Ph. D in Information and Communication Engineering degrees from Anna University, India. He is currently working as a teaching fellow in the Department of Computer Science and Engineering, Anna University, BIT-Campus, Tiruchirappalli, India. His research interests include data mining, wireless networks, parallel computing, mobile computing, computer networks, image processing, software engineering, soft computing, cloud computing, big data analytics, teaching learning process and engineering education, human resource management.

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A Hybrid Dimensionality Reduction Model for Classification of Microarray Dataset

Cited by 13 publications

References 13 publications

Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

A novel community detection based genetic algorithm for feature selection

Dimensionality Reduction for Classification and Clustering

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