Evolutionary sequential genetic search technique‐based cancer classification using fuzzy rough nearest neighbour classifier

Meenachi, L.; Ramakrishnan, S.

doi:10.1049/htl.2018.5041

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Meenachi et al 148 studied the cancer classification model developed from fuzzy‐rough nearest neighbor (FRNN) classifier using the reduced features. The features were reduced using a feature selection algorithm based on the hybridization of evolutionary sequential genetic search algorithm and fuzzy rough set.…”

Section: Machine Learning and Recommendation Systemsmentioning

confidence: 99%

RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

Sharma

Aujla

Bajaj

2019

Int J Communication

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The evolution of intelligent and data-driven systems has pushed for the tectonic transition from ancient medication to human-centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient-centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state-of-the-art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human-centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future. KEYWORDScollaborative filtering-based recommender systems, content-based recommender systems, context-aware recommender system, health care recommender system, Healthcare 4.0, human-centric systems INTRODUCTIONThe increased demand of the health statistics and the variations in looking for information tactics can be pragmatic around the globe. 1 As per the latest study, 81% of the adults in the United States utilize the Internet services, and out of them, 59%

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Section: Machine Learning and Recommendation Systemsmentioning

confidence: 99%

RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

Sharma

Aujla

Bajaj

2019

Int J Communication

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“…Table 7. Comparison of percentage of features used by the proposed method and existing feature-selection algorithms Propos method GATFRO [50] ACTFRO [50] GSFR [51] GLO [52] DEGR [53] FS-JMIE [54] PSO […”

Section: Evaluation Of the Results And Comparison Of Functionsmentioning

confidence: 99%

A New Evolutionary Ensemble Learning of Multimodal Feature Selection from Microarray Data

Nekouie

Romoozi

Esmaeili

2023

Neural Process Lett

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In the last decades, data has grown exponentially with respect to the number of samples and features, which makes feature selection more challenging. An optimization method called multimodal optimization techniques is employed to nd several optimal solutions. The main contribution of multimodal optimization techniques is providing several optimal solutions instead of a single solution. Since multimodal optimization algorithm naturally provides several answers, in this paper the potential and information of several answers are used to address the issues of selecting features from microarray data, using hidden information in the data, and creating an ensemble of classi ers. After pre-processing of data, several optimal feature subsets are obtained by re ybased multimodal optimization algorithm to tap the potential of and information in several answers. The mutual information method is used as the tness function to evaluate the suggested subset of features. Then, each subset of features is used to learn a classi er and such classi ers are learned on data whose features are presented by a multimodal optimization algorithm, and these classi ers are combined. To select a proper combination, a particle swarm optimization algorithm is used. Finally, the algorithm for the datasets of the microarray is evaluated in terms of cancer diagnosis. The algorithmic method e ciency is evaluated by applying it on 11 datasets. The results suggested the superiority and proper function of the multimodal feature selection method compared with other methods.

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“…Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing [14,15] have been criticized mainly for their: 1) O(MN)…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Rough Model Using Genetic Algorithm for Accuracy Enhancement

2021

IJIES

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Evolutionary sequential genetic search technique‐based cancer classification using fuzzy rough nearest neighbour classifier

Cited by 10 publications

References 21 publications

RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

A New Evolutionary Ensemble Learning of Multimodal Feature Selection from Microarray Data

A Fuzzy Rough Model Using Genetic Algorithm for Accuracy Enhancement

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