RETRACTED ARTICLE: A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization

Kaur, Taranjit; Saini, Barjinder Singh; Gupta, Savita

doi:10.1007/s00521-017-2869-z

Cited by 41 publications

(18 citation statements)

References 41 publications

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“…Yang et al [142] modified BA to improve the diversity of the population of bats so that it made a good balance between exploration and exploitation and solve the feature selection problem. To classify the MR brain tumour image, Kaur et al [143] modified BA by combining Fisher and parameter-free bat algorithm for good exploration. The dataset has been taken from UCI repository and applied with SVM classifier.…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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“…To alleviate this constraint, the variation structure was introduced, motivated by the works in [24], [25]. The new position update mechanism is given below [26]:…”

Section: ) Fuzzy Knn(fknn) Classifiermentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur

Gandhi

Panigrahi

2021

IEEE J. Transl. Eng. Health Med.

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Background: Timely and precise identification of COVID-19 is an arduous task owing to the scarcity and inefficiency of the medical test kits. This has resulted in medical professionals turning towards Computed Tomography (CT) scans. Efforts are being made to design deep learning models capable of COVID-19 detection using CT scans. This has certainly reduced the manual intervention in disease detection but reported accuracy is limited. Methods: The present work proposes an automatic system for COVID-19 diagnosis based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier. Typically, features are extracted from the fully connected layer of transfer learned MobileNetv2 that are then employed for FKNN training. The hyper-parameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-arts. Conclusion: The outcomes presented in the paper will aid in speedy detection of this virus at the different phases. Such a detection system will also increase the opportunities for fast recovery of patients worldwide thereby releasing the pressure off the medical professionals and the healthcare system around the world.

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“…By the design principle of image classifier, the common image classification methods can be divided into two categories: those based on production model [2], and those based on discriminant model [3]. The former statistically represents the data distribution and reflects the similarity between images, in the light of the joint probability distribution of image features and classes.…”

Section: Introductionmentioning

confidence: 99%

A Fast Recognition Algorithm of Online Social Network Images Based on Deep Learning

Meng¹,

Mao²,

Zhang³

et al. 2019

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In recent years, a massive number of images have generated on the online social network (OSN). This calls for an efficient and rapid way to extract the information from the OSN images. This paper puts forward an OSN image classification method based on improved deep belief network (DBN) and support vector machine (SVM). In the proposed method, the image classification is enhanced by improving the self-adaptive learning rate based on incremental discrimination of reconstruction error and the weight update criteria with increasing momentum. The effectiveness of our method was confirmed through an image recognition experiment on OSN images obtained from Sina Weibo public platform, in comparison with four commonly used classification methods. The research results shed new light on feature extraction and classification of OSN images.

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RETRACTED ARTICLE: A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization

Cited by 41 publications

References 41 publications

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

A Fast Recognition Algorithm of Online Social Network Images Based on Deep Learning

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