Enhancing learning classifier systems through convolutional autoencoder to classify underwater images

Irfan, Muhammad; Zheng, Jiangbin; Iqbal, Muhammad; Arif, Muhammad

doi:10.1007/s00500-021-05738-w

Cited by 19 publications

(5 citation statements)

References 44 publications

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“…For the classification of real world images Irfan et al [17] combine a deep autoencoder and XCSR to CAXCS. XCSR evolves interpretable rules operating on rich feature maps extracted by the encoder part of an autoencoder.…”

Section: Lcs and Deep Learningmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 and 2021 presentations were accompanied by survey workshop papers, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2021 to 10 March 2022. The 42 publications we review are grouped into six overall topics: Formal theoretic advances, new LCS architectures, LCS-based reinforcement learning, algorithmic improvements to existing LCSs, combinations of LCS and Deep Learning models and, finally, a variety of applications of LCSs. CCS CONCEPTS• Computing methodologies → Rule learning; Genetic algorithms; • General and reference → Surveys and overviews.

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Section: Lcs and Deep Learningmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…Abualigah et al [ 42 ] studied the application of optimization algorithm in text clustering, and studied the accessibility and application of appropriate optimization algorithms for each class. Irfan et al [ 43 ] pointed out that to overcome the inherent limitations of learning classifier system-based systems to high-dimensional problems, the hybrid model designed by the integration of learning classifier system and deep learning methods has been a research hotspot in recent years. Irfan et al [ 44 ] studied the application of brain-inspired lifelong learning model based on neural learning classifier system in underwater data classification and developed a continuous learning system like human beings to improve classification performance.…”

Section: Related Workmentioning

confidence: 99%

An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times

Chen

Niu

et al. 2022

Cluster Comput

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Surgical case scheduling is a key issue in the field of medician, which is a challenging work because of the difficulty in assigning resources to patients. This study regards the surgical case scheduling problem as a flexible job shop scheduling problem (FJSP). Considering the switching and preparation time of patients in different stage, an improved multi-objective imperialist competitive algorithm (IMOICA), which adopts the non-dominant sorting method, is proposed to optimize the whole scheduling. First, the social hierarchy strategy is developed to initialize the empire. Then, to enhance the global search ability of the algorithm, the concept of attraction and repulsion (AR) is introduced into the assimilation strategy. Moreover, to increase the diversity of the population, the revolution strategy is utilized. Finally, the variable neighborhood search (VNS) strategy is embedded to improve its exploitation capacity further. Experiments show that scheduling in advance saves time and cost, and IMOICA can solve the surgical case scheduling problem studied efficiently.

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“…Finally in the last step, multiscale pyramidal fusion of the inputs and weight maps are applied. The authors in [19] adopted a novel learning classifier system (LCS) which accurately classifies large-size of underwater images using a novel classification convolution autoencoder (CCAE). CCAE makes use the combination of the autoencoder and classifier in order to enhance the accuracy and precision of system.…”

Section: Related Workmentioning

confidence: 99%

“…To benchmarking and evaluating the effectiveness of proposed system, we compare the results of the proposed model with the quality of different approaches [19,20]. Following tables in simulation section indicates the accuracy of the proposed method and the accuracy of two the state-of-the-art methods.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

An open-set framework for underwater image classification using autoencoders

Akhtarshenas

Toosi

2022

SN Appl. Sci.

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In this paper, we mainly intend to address the underwater image classification problem in an open-set scenario. Image classification algorithms have been mostly provided with a small set of species, while there exist lots of species not available to the algorithms or even unknown to ourselves. Thus, we deal with an open-set problem and extremely high false alarm rate in real scenarios, especially in the case of unseen species. Motivated by these challenges, our proposed scheme aims to prevent the unseen species from going to the classifier section. To this end, we introduce a new framework based on convolutional neural networks (CNNs) that automatically identifies various species of fishes and then classifies them into certain classes using a novel technique. In the proposed method, an autoencoder is employed to distinguish between seen and unseen species. To clarify, the autoencoder is trained to reconstruct the available species with high accuracy and filter out species that are not in our training set. In the following, a classifier based on EfficientNet is trained to classify the samples that are accepted by the autoencoder (AE), i.e. the samples that have small reconstruction error. Our proposed method is evaluated in terms of precision, recall, and accuracy and compared to the state-of-the-art methods utilizing WildFish dataset. Simulation results reveal the supremacy of the proposed method.

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Enhancing learning classifier systems through convolutional autoencoder to classify underwater images

Cited by 19 publications

References 44 publications

An overview of LCS research from 2021 to 2022

An overview of LCS research from 2021 to 2022

An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times

An open-set framework for underwater image classification using autoencoders

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