A Survey on Evolutionary Machine Learning

Al-Sahaf, Harith; Bi, Ying; Chen, Qi; Lensen, Andrew; Yi, Ming; Sun, Yuhan; Tran, Binh Q.; Xue, Bing; Zhang, Mengjie

doi:10.26686/wgtn.12493928

Cited by 23 publications

(39 citation statements)

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“…Performing all these steps manually can take a great deal of time and expertise, so instead, we leverage existing tools to improve both the speed and accuracy of the process, resulting in much greater efficiency [6]. Additionally, this opens up the field of ML to those without ML domain specific knowledge [7]. Attempts have even been made to crowdsource and benchmark previous ML studies to use as a reference for future work [8].…”

Section: Automlmentioning

confidence: 99%

Optimizing Recommendations for Clustering Algorithms Using Meta-Learning

Jilling

Álvarez

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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The field of machine learning (ML) has seen explosive growth over the past decade, largely due to increases in technology and improvements of implementations. As powerful as ML solutions can be, they are still reliant on human input to select the optimal algorithms and parameters. This process is typically done by trial and error, as researchers will select a number of algorithms and choose whichever provides the most desirable result. This study will use a process called meta-learning to evaluate and analyze datasets and extract a series of meta-features. These features can then be used to intelligently recommend an optimal algorithm, without the cost of having to manually run the algorithm. To accomplish this, we will experiment using 230 datasets and determine their expected outcomes using only the meta-features. The outcomes being optimized are performance (accuracy) and runtime. Results are ranked in terms of performance and runtime and we can determine how accurately the learning model was able to choose the optimal algorithm for each objective. Additionally, we also run tests to determine the optimal learning rate and weight decay to use when training.

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Section: Automlmentioning

confidence: 99%

Optimizing Recommendations for Clustering Algorithms Using Meta-Learning

Jilling

Álvarez

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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“…Sutskever et al [34] proposed a multilayer LSTM in the RNN for translating English to French which achieves outstanding accuracy and brings the approach into notice [78]. The use of Evolutionary Computing (EC) for machine learning is presented from various perspectives such as feature selection and classification, regression and deep learning [79]. In deep learning the EC provides an optimal solution to machine learning for reducing the cost such as a significant amount of time and domain expertise [80]- [82].…”

Section: Related Workmentioning

confidence: 99%

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

2020

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Safe Routes to School is very important for students to have good physical and psychologically healthy in school life. For providing safe routes based on risk analysis, finding out dangerous points and areas can be a target to avoid dangerous locations by pedestrians and drivers. However, analyzing the risk assessment to derive the safe routes requires a large amount of data with a certain time of observation by experts. Deep learning is a solution to provide information regarding safe routes based on expert knowledge. In this paper, we propose a risk assessment inference approach using a Recurrent Neural Network (RNN) model with Long-Short Term Memory (LSTM) cells based on geographical information for safe routes to school. However, geographical information including coordinates is difficult used in learning-based inference models because of the series of float values. For training the RNN model with the geographical data, coordinates of routes and danger points are translated to be geohash through the geohash converter. The geohash data with other data of features are fused and inputted to the one-hot encoder. The one-hot encoded data is used in the inputs of the RNN model to train the LSTMs. The input data of the training model is derived by the risk index model that is proposed to calculate the risk index based on distances of route coordinates and danger points. Therefore, the risk index is correlated with the training dataset. Through the proposed inference approach, the geographical information including multiple coordinates is enabled to be trained by RNN as a geohash-based input string. Moreover, the input string with other features is fused to support the one-hot encoding to get a better result in RNN models.

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“…Evolutionary machine learning can be applied to a plethora of problem domains. The Evolutionary ensemble learning is one of the emerging topics according to a survey of 2019 [Al-Sahaf et al, 2019]. As such, we can expect new developments in this field with new ways to use evolutionary algorithms to our advantage.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

EDGE: Evolutionary Directed Graph Ensembles

Fontes

Silva

2019

HIS

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Classification tasks are being tackled in a plethora of scientific fields, such as astronomy, finance, healthcare, human mobility, and pharmacology, to name a few. Classification is defined as a supervised learning approach that uses labeled data to assign instances to classes. A common approach to tackle these tasks are ensemble methods. These are methods that employ a set of models, instead of just one and combine the predictions of every model to obtain the prediction of the whole. Common obstacles in ensemble learning are the choice of base models to use and how best to aggregate the predictions of each individual to produce the ensemble's prediction. It is also expected to mitigate the weaknesses of its members while pooling their strengths together. It is in this context that Evolutionary Directed Graph Ensembles (EDGE) thrives. EDGE is a machine learning tool based on social dynamics and modeling of trust in human beings using graph theory. Evolutionary Algorithms are used to evolve ensembles of models that are arranged in a directed acyclic graph structure. The connections in the graph map the trust of each node in its predecessors. The novelty in such an approach stems from the fusion of ensemble learning with graphs and evolutionary algorithms. A limitation of EDGE is that it focuses only on changing the topology of the graph ensembles, with the authors of hypothesizing about using the learned graphs for other tasks. Our objective is to tackle the limitations of the original proposal of EDGE and bestow upon it new capabilities to improve its predictive power. This project proposes a method for updating the weights of the connections between nodes of the graph ensembles, such that the strength of the relationships between nodes evolves over time. Inspired by the notion of meta-learning, we also propose a methodology for saving learned graphs and bootstrapping different datasets with evolved graphs. We endow EDGE with bootstrapping capabilities while investigating a suitable similarity metric for dataset choice based on the extraction of meta-features from datasets. When compared to the original EDGE, our weight evolution approach improved on 3 of the 4 datasets by an average margin of 4.20 percentage points, where the loss in the fourth dataset was of about 0.3 percentage points. Once compared with a baseline suite of models, ours achieved the best value in 34 of the 38 datasets, with gains as substantial as 30 percentage points. The bootstrap was shown to be effective in improving the prediction power, with the exploitation of previous runs improved the results on 19 out of 21 datasets. The contributions can be summarized as a novel way to evolve graph ensembles, by also evolving the weights between nodes of the graphs, coupled with the idea of bootstrapping any dataset using previous runs from other datasets. The analysis of dataset choice for the bootstrapping lead to the proposal of a similarity metric between datasets that can be used to facilitate the choice for bootstrapping, without exhaustive or...

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A Survey on Evolutionary Machine Learning

Cited by 23 publications

References 0 publications

Optimizing Recommendations for Clustering Algorithms Using Meta-Learning

Optimizing Recommendations for Clustering Algorithms Using Meta-Learning

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

EDGE: Evolutionary Directed Graph Ensembles

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