Automatic Machine Learning: An Exploratory Review

Patibandla, R. S. M. Lakshmi; Srinivas, V. Sesha; Mohanty, Sachi Nandan; Pattanaik, Chinmaya Ranjan

doi:10.1109/icrito51393.2021.9596483

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…Reviews Task Formulation [30,6] Prediction Engineering [30,6] Data Preparation [14,21,9,39,20,3,6,26,25] Feature Engineering [30,24,23,14,21,9,39,20,3,35,6,31,26,25] Model Selection [30,24,23,14,21,9,39,33,32,20,3,35,8,6,31,36,26,25] Hyperparameter Opt. [30,24,23,14,21,9,39,…”

Section: Phasesmentioning

confidence: 99%

“…Within this process we define two types of data preparation, those that increase the number of data points (e.g. data collection, data augmentation) [14,21,9,3,6,26] and those that do not (e.g. data cleaning, data inputation, data standardization) [14,21,39,20,3,6,26,25].…”

Section: Phasesmentioning

confidence: 99%

“…data collection, data augmentation) [14,21,9,3,6,26] and those that do not (e.g. data cleaning, data inputation, data standardization) [14,21,39,20,3,6,26,25]. The output of this phase is typically a curated dataset ready to be used for feature engineering.…”

Section: Phasesmentioning

confidence: 99%

See 2 more Smart Citations

A General Recipe for Automated Machine Learning in Practice

Ceferino

2022

Advances in Artificial Intelligence – IBERAMIA 2022

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Automated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents a great opportunity for the practice of applied machine learning. However, there is very little information on how to design an AutoML system in practice. Most of the research focuses on the problems facing optimization algorithms and leaves out the details of how that would be done in practice. In this paper, we propose a frame of reference for building general AutoML systems. Through a narrative review of the main approaches in the area, our main idea is to distill the fundamental concepts in order to support them in a single design. Finally, we discuss some open problems related to the application of AutoML for future research.

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Section: Phasesmentioning

confidence: 99%

Section: Phasesmentioning

confidence: 99%

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A General Recipe for Automated Machine Learning in Practice

Ceferino

2022

Advances in Artificial Intelligence – IBERAMIA 2022

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“…To reduce the above risks, it is important to per robust monitoring and testing [4]. The main components that MLOps implementa offer to ML software production are: (a) collaboration and communication, (b) autom The first stage concerns the data preprocessing and is carried out in terms of [4,8,22,23,25]: (a) data collection approaches that generate a proper data set to train an ML model, (b) data visualization techniques, which are employed to assist the data analyst in comprehending the internal data structure, (c) data cleaning mechanisms that eliminate the noise existing in the available data set and avoid the creation of compromised models, and (d) data augmentation procedures that are used to extend the original data set and enhance the model's robustness by avoiding overfitting problems. The second stage uses feature engineering mechanisms to extract features from the raw data and to make more convenient the design of ML algorithms in effectively describing the data [4].…”

Section: Introductionmentioning

confidence: 99%

“…Structure identification concerns the determination of the ML approach that best solves the problem at hand. Typical structures commonly used are neural networks or deep neural networks [4,9,25]. The next process is to optimize the architecture and the set of hyperparameters (e.g., learning rate, number of layers, etc.)…”

Section: Introductionmentioning

confidence: 99%

Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline

et al. 2023

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In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks.

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A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

Demertzis

Iliadis

2023

Applied Sciences

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Artificial intelligence is the branch of computer science that attempts to model cognitive processes such as learning, adaptability and perception to generate intelligent behavior capable of solving complex problems with environmental adaptation and deductive reasoning. Applied research of cutting-edge technologies, primarily computational intelligence, including machine/deep learning and fuzzy computing, can add value to modern science and, more generally, to entrepreneurship and the economy. Regarding the science of civil engineering and, more generally, the construction industry, which is one of the most important in economic entrepreneurship both in terms of the size of the workforce employed and the amount of capital invested, the use of artificial intelligence can change industry business models, eliminate costly mistakes, reduce jobsite injuries and make large engineering projects more efficient. The purpose of this paper is to discuss recent research on artificial intelligence methods (machine and deep learning, computer vision, natural language processing, fuzzy systems, etc.) and their related technologies (extensive data analysis, blockchain, cloud computing, internet of things and augmented reality) in the fields of application of civil engineering science, such as structural engineering, geotechnical engineering, hydraulics and water resources. This review examines the benefits and limitations of using computational intelligence in civil engineering and the challenges researchers and practitioners face in implementing these techniques. The manuscript is targeted at a technical audience, such as researchers or practitioners in civil engineering or computational intelligence, and also intended for a broader audience such as policymakers or the general public who are interested in the civil engineering domain.

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Automatic Machine Learning: An Exploratory Review

Cited by 4 publications

References 15 publications

A General Recipe for Automated Machine Learning in Practice

A General Recipe for Automated Machine Learning in Practice

Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

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