A review on the self and dual interactions between machine learning and optimisation

Song, Heda; Triguero, Isaac; Özcan, Ender

doi:10.1007/s13748-019-00185-z

Cited by 70 publications

(38 citation statements)

References 150 publications

(213 reference statements)

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“…ML and optimisation are two important fields of AI interacting frequently with each other in order to improve learning and/or search capabilities [80]. In the context of our work, the optimization process focuses on finding the best (optimal) model from a group of alternative candidates based on the already described evaluation metrics (Section IV-D and Equations 25,26).…”

Section: E Model Optimization and Selectionmentioning

confidence: 99%

Deep Learning for Proactive Network Monitoring and Security Protection

et al. 2020

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The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring.

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Section: E Model Optimization and Selectionmentioning

confidence: 99%

Deep Learning for Proactive Network Monitoring and Security Protection

et al. 2020

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“…When comes to integrate machine learning and metaheuristics [ 9 , 10 ], two large groups can be mainly indicated. The first group, corresponds metaheuristic techniques improve the performance of machine learning algorithms.…”

Section: Binarizating Continuous Metaheuristicsmentioning

confidence: 99%

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

Vásquez

Lemus-Romani

Crawford

et al. 2020

Lecture Notes in Computer Science

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Metaheuristics are used to solve high complexity problems, where resolution by exact methods is not a viable option since the resolution time when using these exact methods is not acceptable. Most metaheuristics are defined to solve problems of continuous optimization, which forces these algorithms to adapt its work in the discrete domain using discretization techniques to solve complex problems. This paper proposes data-driven binarization approaches based on clustering techniques. We solve different instances of Knapsack Problems with Galactic Swarm Optimization algorithm using this machine learning techniques.

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“…Machine learning aims to extract the hidden patterns from data based on algorithms. Due to the rapid advance of technology and communication, these algorithms have drawn widespread attention and have been successfully applied to many real-world problems [32]. The machine learning approach can be applied to different optimization problems ranging from wind energy decision system [33], socially aware cognitive radio handovers [34], and truck scheduling at cross-docking terminals [35,36] to the sustainable supply chain network integrated with vehicle routing [37].…”

Section: Image Classification Based On Multi-kernel Learningmentioning

confidence: 99%

A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

Zeng

2019

Information

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Extracting road from high resolution remote sensing (HRRS) images is an economic and effective way to acquire road information, which has become an important research topic and has a wide range of applications. In this paper, we present a novel method for road extraction from HRRS images. Multi-kernel learning is first utilized to integrate the spectral, texture, and linear features of images to classify the images into road and non-road groups. A precise extraction method for road elements is then designed by building road shaped indexes to automatically filter out the interference of non-road noises. A series of morphological operations are also carried out to smooth and repair the structure and shape of the road element. Finally, based on the prior knowledge and topological features of the road, a set of penalty factors and a penalty function are constructed to connect road elements to form a complete road network. Experiments are carried out with different sensors, different resolutions, and different scenes to verify the theoretical analysis. Quantitative results prove that the proposed method can optimize the weights of different features, eliminate non-road noises, effectively group road elements, and greatly improve the accuracy of road recognition.

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A review on the self and dual interactions between machine learning and optimisation

Cited by 70 publications

References 150 publications

Deep Learning for Proactive Network Monitoring and Security Protection

Deep Learning for Proactive Network Monitoring and Security Protection

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

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