A Review on Automated Machine Learning (AutoML) Systems

Nagarajah, Thiloshon; Poravi, Guhanathan

doi:10.1109/i2ct45611.2019.9033810

Cited by 56 publications

(39 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The model performance mostly depends on a set of hyperparameters that make up the algorithm. Hyperparameters are tuned specifically to that dataset, with some techniques like Regression Trees, and Gaussian Processes [40]. Bayesian optimization has been applied as a successful candidate for hyperparameter tuning, which fits a probabilistic model to capture the relationship between hyperparameters' setting and their measured performance.…”

Section: Description Of Automl Modelmentioning

confidence: 99%

Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML

et al. 2022

View full text Add to dashboard Cite

Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA’s LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets.

show abstract

Section: Description Of Automl Modelmentioning

confidence: 99%

Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The aim of this study is to categorize and review the methodologies relating to adaptiveness in ANNs. Although there are some reviews conducted on Continual Learning [16], [18], AutoML [53]- [55], and NAS [50], [56]- [58], the intersection between AutoML and Continual Learning has not been formalized or reviewed despite the significant number of existing models in that area. A few related works have established a basis for such a paradigm, but with limited scopes [15], [59].…”

Section: A Use-cases and Motivationmentioning

confidence: 99%

A Review on Plastic Artificial Neural Networks: Exploring the Intersection between Neural Architecture Search and Continual Learning

Shahawy¹,

Benkhelifa²,

White³

2022

Preprint

View full text Add to dashboard Cite

Despite the significant advances achieved in Artificial Neural Networks (ANNs), their design process remains notoriously tedious, depending primarily on intuition, experience and trial-and-error. This human-dependent process is often time-consuming and prone to errors. Furthermore, the models are generally bound to their training contexts, with no considerations of changes to their surrounding environments. Continual adaptability and automation of neural networks is of paramount importance to several domains where model accessibility is limited after deployment (e.g IoT devices, selfdriving vehicles, etc). Additionally, even accessible models require frequent maintenance post-deployment to overcome issues such as Concept/Data Drift, which can be cumbersome and restrictive. The current state of the art on adaptive ANNs is still a premature area of research; nevertheless, Neural Architecture Search (NAS), a form of AutoML, and Continual Learning (CL) have recently gained an increasing momentum in the Deep Learning research field, aiming to provide more robust and adaptive ANN development frameworks. This study is the first extensive review on the intersection between AutoML and CL, outlining research directions for the different methods that can facilitate full automation and lifelong plasticity in ANNs.

show abstract

“…17 AutoML is an automated process of ML that custom-builds models. 28 This is a growing area of research because AutoML reduces the demand for human intervention. 29,30 Furthermore, the AutoML method includes data preparation, feature engineering, model generation, and model evaluation (for a review, see [31,32].…”

Section: And Automated Machine Learning (Automl)mentioning

confidence: 99%

“…AutoML is an automated process of ML that custom‐builds models 28 . This is a growing area of research because AutoML reduces the demand for human intervention 29,30 .…”

Section: Introductionmentioning

confidence: 99%

AutoML classifier clustering procedure

Koren

Hallin

Koren

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Recommendation systems are one of the main applications of machine learning (ML) used across different industries. This paper presents a new automated machine learning (AutoML) method of providing recommendations by processing data sets using ML algorithms, targeting, and offering cluster recommendations for new observations and as a new decision support method. The AutoML conducts a complete procedure and includes analysis and division of data into an efficient number of clusters. We apply the k‐means, using the elbow method to calculate costs per cluster, followed by analyzing the allocation of the data into the clusters, thus providing a method for prediction and for allocating new observations to the relevant clusters (knn). This study includes two experiments using the complete AutoML procedure conducted on a data set, with more than two million records and dozens of attributes. This was done to demonstrate how the AutoML method can be implemented and successfully run with a high‐capacity analysis procedure. The motivation was to analyze, examine, assign, and integrate new observations into existing clusters that have been defined. The results showed that the AutoML method provided efficient recommendations for new observations with an accuracy rate of 99.99%. Hence, the AutoML procedure can offer a full system for any organization to efficiently split existing data into clusters, assign to clusters, and predict the cluster allocation of new observations. The significant contribution of this study is a simple method that can achieve fast and high accuracy clustering for ongoing (new) classified data acquired by an organization.

show abstract

A Review on Automated Machine Learning (AutoML) Systems

Cited by 56 publications

References 4 publications

Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML

Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML

A Review on Plastic Artificial Neural Networks: Exploring the Intersection between Neural Architecture Search and Continual Learning

AutoML classifier clustering procedure

Contact Info

Product

Resources

About