scholarly journals A Novel Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles

2018 ◽  
Author(s):  
Joao C. Xavier-Junior ◽  
Alex A. Freitas ◽  
Antonino Feitosa-Neto ◽  
Teresa B. Ludermir
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Classifier Ensembles ◽  
Automated Machine Learning

scholarly journals An evolutionary algorithm for automated machine learning focusing on classifier ensembles: An improved algorithm and extended results

2020 ◽  
Vol 805 ◽  
pp. 1-18
Author(s):  
João C. Xavier-Júnior ◽  
Alex A. Freitas ◽  
Teresa B. Ludermir ◽  
Antonino Feitosa-Neto ◽  
Cephas A.S. Barreto
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
Improved Algorithm

scholarly journals Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Borja Espejo-Garcia ◽  
Ioannis Malounas ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Plant Protection ◽  
Crop Protection ◽  
Identification Problem ◽  
Learning System ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
A New Technique ◽  
Plant Seedlings ◽  
And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

scholarly journals A Graph-based Evolutionary Algorithm for Automated Machine Learning

2020 ◽  
Author(s):  
Fei Qi ◽  
Zhaohui Xia ◽  
Gaoyang Tang ◽  
Hang Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Parameter Optimization ◽  
State Of The Art ◽  
The State ◽  
Complex Structures ◽  
Architecture Evolution ◽  
Automated Machine Learning ◽  
Art Performance

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

scholarly journals Different mutation and crossover set of genetic programming in an automated machine learning

2020 ◽  
Vol 9 (3) ◽  
pp. 402
Author(s):  
Suraya Masrom ◽  
Masurah Mohamad ◽  
Shahirah Mohamed Hatim ◽  
Norhayati Baharun ◽  
Nasiroh Omar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Parameters Optimization ◽  
Accuracy Score ◽  
Learning Improvement ◽  
Different Types ◽  
Automated Machine Learning ◽  
Prediction Problems ◽  
Selection Of

<span lang="EN-US">Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm.  As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.</span>

Bandit-Based Automated Machine Learning

2018 ◽  
Author(s):  
Silvia Cristina Nunes das Dores ◽  
Carlos Soares ◽  
Duncan Ruiz
Keyword(s):  
Machine Learning ◽  
Automated Machine Learning

scholarly journals Automated Machine-Learning Radiotherapy Planning for Pediatric and Adult Brain Tumours

2021 ◽  
Vol 52 (2) ◽  
pp. S3
Author(s):  
Grace Tsui ◽  
Derek S. Tsang ◽  
Chris McIntosh ◽  
Thomas G. Purdie ◽  
Glenn Bauman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumours ◽  
Adult Brain ◽  
Radiotherapy Planning ◽  
Automated Machine Learning

scholarly journals Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Milos Kotlar ◽  
Marija Punt ◽  
Zaharije Radivojevic ◽  
Milos Cvetanovic ◽  
Veljko Milutinovic
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Anomaly Detection ◽  
Learning Model ◽  
Machine Learning Model ◽  
Automated Machine Learning
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