Hybrid Efficient Genetic Algorithm for Big Data Feature Selection Problems

Mohammed, Tareq Abed; Bayat, Oğuz; Uçan, Osman N.; Alhayali, Shaymaa

doi:10.1007/s10699-019-09588-6

Cited by 15 publications

(15 citation statements)

References 35 publications

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“…In 2019, Mohammed et al 27 have proposed an innovative approach with the intention of providing a promising solution for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

“…In 2019, Mohammed et al 27 have proposed an innovative approach with the intention of providing a promising solution for the FS problem. The new solution was formulated using hybrid and efficient genetic algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 tabulates the major advantages and challenges of the most fascinating literature works discussed in the literature section. The Genetic Algorithm in Reference 27 has exhibited higher classification performance. But, here the convergence, which is being the major challenge in most of the works, is not being satisfied.…”

Section: Literature Reviewmentioning

confidence: 99%

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Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Selvi¹,

Valarmathi²,

Devadas

2022

Concurrency and Computation

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Many fields function with large databases constitute a high number of features. Feature selection strategies seek to exclude the features that are distracting, repetitive, or unnecessary, as they can degrade the classification results. Existing approaches lack the scalability needed to handle the datasets with millions of instances and they do not obtain favorable results in a timely manner. This study uses a unique feature selection approach based on an upgraded optimization model and deep machine learning‐based data classification. “(a) Feature extraction, (b) optimal feature selection, and (c) classification” are the three stages of the proposed model. Initially, the extracted big‐datasets are efficiently handled by the parallel pool map‐reduce architecture. Several features from the input big‐data are extracted using feature extraction (FE) approaches such as the suggested Tri‐Kernel principal component analysis (TK‐PCA), linear discriminant analysis, and linear square regression. Furthermore, the data obtained characteristics may contain data that is irrelevant, out‐of‐date, or noisy. The computing cost rises due to the larger feature space. As a result, the best features are selected using a new optimization technique known as Levy Adapted SLnO (LA‐SLnO), which is a superior variant of the original SLnO algorithm. This selection of appropriate features improves the classification accuracy. For classification, Convolutional Neural Network is used in this work. Finally, a comparative evaluation is undergone to validate the efficiency of the proposed model.

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“…In 2019, Mohammed et al 27 have proposed an innovative approach with the intention of providing a promising solution for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Selvi¹,

Valarmathi²,

Devadas

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The patient dataset included 457914 cases and nine tables. Each table had different features, and many techniques could be implemented, such as semantic coordination for intelligent databases [24], feature selection problems using genetic algorithms [25], and new gene-weight mechanisms [26]. Some features were connected with other tables to build datasets describing the main attribute, as mentioned in the next section.…”

Section: B Available Features Of Patientsmentioning

confidence: 99%

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

2020

Self Cite

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Recently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications. INDEX TERMS Health dataset analysis, machine learning, methodology, software development management, software engineering.

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“…The feature selection process becomes an important step in many Data Mining and Machine Learning algorithms to reduce the dimensionality of the optimization problems in question. It is with this in mind that researchers Tareq Abed Mohammed, OguzBayat, Osman N. Uçan and ShaymaaAlhayali in their paper [65], proposed a set of hybrid and effective genetic algorithms to solve the characteristic selection problem. These algorithms use a new gene weighted mechanism capable of adaptively classifying characteristics into three types (relative strong, weak and unstable) during the algorithm's evolution.…”

Section: Optimization Problem With Ga-big Datamentioning

confidence: 99%

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

2020

IJATCSE

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Artificial intelligence (AI) techniques play an important role in the development and optimization of complex problems in various fields. This study presents a systematic analysis of previously publications on the use of AI techniques to optimize the NP-hard problems in the area of human resource assignment to multi-constrained activities in order to increase the productivity and performance of a company, and in the area of vehicle routing in order to cost minimization. These two problems belonged to the NP-hard family. Among these AI techniques used for a better optimization of this type of problems, we discuss the genetic algorithms (GA), hybrid GA, parallel GA, GA with Big Data and improved AI techniques with special features are also discussed and presented through the different contributions of the researchers.

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Hybrid Efficient Genetic Algorithm for Big Data Feature Selection Problems

Cited by 15 publications

References 35 publications

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

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