A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines

Alirezaei, Mahsa; Niaki, Seyed Taghi Akhavan; Niaki, Seyed Armin Akhavan

doi:10.1016/j.eswa.2019.02.037

Cited by 42 publications

(19 citation statements)

References 30 publications

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“…Also in [18], Alirezaei et al proposed a wrapper-based method for feature selection using four meta-heuristic algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), FA, PSO, and Imperialist Competitive Algorithm (ICA), with bi-objective fitness for reducing features dimensionality and improving classification accuracy in diabetes diagnosis. The SVM classifier achieved classification accuracies of 100%, 100%, 98.2%, and 94.6% when evaluating the features selected by FA, ICA, NSGA-II, and PSO, respectively, using the PIMA Indian Type-2 diabetes dataset obtained from UCI Machine Learning Datasets Repository.…”

Section: A Feature Selection/fusion Methodsmentioning

confidence: 99%

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

2020

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The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an indicator of potential structural deterioration according to its severity. This paper presents a novel crack severity recognition system using a hybrid filter-wrapper with multi-objective optimization feature selection method. The proposed approach comprises two main components, namely, (1) feature extraction based on hand-crafted feature engineering and CNN-based deep feature learning and (2) feature selection using hybrid filter-wrapper with a multi-objective improved salp swarm optimization. The proposed approach is trained and validated by utilizing 10 representative UCI datasets and 4 datasets of crack images. The obtained experimental results show that the proposed system enhances the performance of crack severity recognition with ≈ 37% and ≈ 31% increase in recognition average accuracy and F-measure, respectively. Also, a reduction rate of ≈ 67% is achieved in the extracted feature set with all the tested datasets compared to the conventional classification approaches using the whole set of features. Moreover, the proposed approach outperforms other approaches with classical feature selection methods in terms of feature reduction rate and computational time. It is noticed that using VGG16 learned features outperforms using the fused hand-crafted features by 17.7%, 15.9%, and 23.5% for fine, moderate, and severe crack recognition, respectively. The significance of this paper is to investigate and highlight the impact of applying multi-feature dimensionality reduction through adopting hybrid filter-wrapper with multi-objective optimization methods for feature selection considering the case study of crack severity recognition for SHM.

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Section: A Feature Selection/fusion Methodsmentioning

confidence: 99%

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

2020

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“…It has less computational complexity, considers elitism, systematically preserves the diversity of Pareto-optimal solutions and adaptively handles the problem constraints (Deb and Jain, 2012). These features have made NSGA-II one of the most popular multi-objective optimisation algorithms in the literature with applications ranging from scheduling to diabetes diagnosis (Alirezaei et al, 2019). Different test problems from previous studies applying NSGA-II were compared in Deb et al (2002), showing that NSGA-II outperforms algorithms such as Pareto Archived Evolution Strategy (PAES) and Strength Pareto Evolutionary Algorithm (SPEA) in obtaining a more diverse set of solutions.…”

Section: Solution Proceduresmentioning

confidence: 99%

The bi-objective periodic closed loop network design problem

Mamaghani

Davari

2020

Expert Systems with Applications

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…On this basis, a non-dominant ranking genetic algorithm with elitism selection strategy was chosen to solve this problem. The high performance of the NSGA-II algorithm [41] in searching the Pareto solution depends on its evolution mechanism, which mainly includes fast non-dominant ranking, congestion distance, and an elitism selection strategy based on non-dominant ranking. In comparison with the other multi-objective algorithms, the NSGA-II algorithm has some advantages.…”

Section: Design Of the Solving Algorithmmentioning

confidence: 99%

Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

et al. 2019

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High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.

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A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines

Cited by 42 publications

References 30 publications

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

The bi-objective periodic closed loop network design problem

Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

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