Using NSGA‐III for optimising biomedical ontology alignment

Xue, Xingsi; Lu, Jiawei; Chen, Junfeng

doi:10.1049/trit.2019.0014

Cited by 46 publications

(23 citation statements)

References 36 publications

(36 reference statements)

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“…(3) Sort I P ; (4) / * Selection * / (5) set F P � I P ; / * F P denotes final population * / (6) while c e ! � 0orl G �� true do 7/ * c e and l G represent children elimination and last generation * / (8) Generate random c; / * c denotes children * / (9) set c e � 0; (10) for eachc do (11) Compute the fitness of c; (12) if f c ≤ f F P [1] then (13) remove c; (14) set c e + � 1; (15) else (16) set F P � c; (17) end (18) end (19) / * Mutation * / (20) for crossover do (21) select c 1 and c 2 randomly; / * c 1 , c 2 , and c 3 are Mathematical Problems in Engineering ranked [32]. Finally, the most nondominated solution is returned as initial parameters of CNN.…”

Section: Multiobjective Fitness Functionmentioning

confidence: 99%

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Shukla

Sandhu

Ahirwar

et al. 2021

Mathematical Problems in Engineering

136

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COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

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Section: Multiobjective Fitness Functionmentioning

confidence: 99%

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Shukla

Sandhu

Ahirwar

et al. 2021

Mathematical Problems in Engineering

136

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show abstract

“…Ontology matching is well suited to solve the problems arising from semantic ambiguity and large data volume in transportation data [21][22][23]. Benvenuti et al [24] integrated Transmodel ontologies and KPIOnto to facilitate the study of public road monitoring systems.…”

Section: Related Workmentioning

confidence: 99%

Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm

Xue

Wang

Zhang

et al. 2021

Journal of Advanced Transportation

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The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques.

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“…He et al [6] proposed to utilize artificial bee colony algorithm (ABC) to optimize all the parameters in the matching process, whose results are better than the EA-based matchers. More recently, Xue et al [17] proposed a new approach that uses NSGA-III [18] to combine various similarity measures without tuning the aggregating parameters. However, when the scale of the similarity measures becomes huge, e.g., more than 50 similarity measures, this approach could be inefficient.…”

Section: Swarm Intelligence Algorithm-based Ontology Matching Techniquementioning

confidence: 99%

Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution

Xue

Chen

2020

Discrete Dynamics in Nature and Society

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Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a MOP, and then a compact multiobjective particle swarm optimization algorithm driven by knee solution (CMPSO-K) is proposed to address it. In particular, a compact evolutionary mechanism is proposed to efficiently optimize the alignment’s quality, and a max-min approach is used to determine the PF’s knee solution. In the experiment, three biomedical tracks provided by Ontology Alignment Evaluation Initiative (OAEI) are used to test CMPSO-K’s performance. The comparisons with OAEI’s participants and PSO-based matching technique show that CMPSO-K is both effective and efficient.

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Using NSGA‐III for optimising biomedical ontology alignment

Cited by 46 publications

References 36 publications

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm

Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution

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