322 Hits in 13.2 sec

Meta-learning approaches to selecting time series models

Ricardo B.C. Prudêncio, Teresa B. Ludermir
2004 Neurocomputing  
We present here an original work that applies meta-learning approaches to select models for time-series forecasting.  ...  Following, we used the NOEMON approach, a more recent work in the meta-learning area, to rank three models used to forecast time series of the M3-Competition (case study II).  ...  Hence, landmarking would be an economic approach to the data characterization step and could provide useful information for the meta-learning process.  ... 
doi:10.1016/j.neucom.2004.03.008 fatcat:i4ivdxpe5zam3dvvrlhr2yvgyy

A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems [article]

Abbas Raza Ali, Marcin Budka, Bogdan Gabrys
2020 arXiv   pre-print
In that scenario and within an on-line predictive system, there are several tasks where Meta-learning can be used to effectively facilitate best recommendations including 1) pre-processing steps, 2) learning  ...  The exponential growth of volume, variety and velocity of data is raising the need for investigations of automated or semi-automated ways to extract useful patterns from the data.  ...  Some implementation issues were also addressed which 13 k=1 14 Neural Network forecasting competition, included: 1) the selection of datasets; 2) the selection  ... 
arXiv:2007.10818v1 fatcat:4jzeippeyjbxfdbza3h5222xey

Categorization of Factors Affecting Classification Algorithms Selection

Mariam Moustafa Reda, Mohammad Nassef, Akram Salah
2019 International Journal of Data Mining & Knowledge Management Process  
A lot of classification algorithms are available in the area of data mining for solving the same kind of problem with a little guidance for recommending the most appropriate algorithm to use which gives  ...  As a way of optimizing the chances of recommending the most appropriate classification algorithm for a dataset, this paper focuses on the different factors considered by data miners and researchers in  ...  No study of the impact of different factors on the selection process was carried out. [35] carried out a survey for meta-learning with landmarking.  ... 
doi:10.5121/ijdkp.2019.9401 fatcat:xualkulnmzen5g2tzw2thhe4re

Metalearning: a survey of trends and technologies

Christiane Lemke, Marcin Budka, Bogdan Gabrys
2013 Artificial Intelligence Review  
This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing  ...  the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.  ...  Acknowledgments The research leading to these results has received funding from the European Union 7th Framework Programme (FP7/2007(FP7/ -2013 under grant agreement no. 251617.  ... 
doi:10.1007/s10462-013-9406-y pmid:26069389 pmcid:PMC4459543 fatcat:ckmfzs3xszbltjqmzs4j7uyuha

Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons

Dabin Zhang, Shanying Chen, Liwen Ling, Qiang Xia
2020 IEEE Access  
features lead to a different selection of the optimal models.  ...  , feature reduction is a workable approach to further improve the performance of the model selection framework; and thirdly, for bean and pig grain products, different distributions of the time series  ...  [12] considered landmarking as features in an empirical study on NN3 data. Talagala, et al.  ... 
doi:10.1109/access.2020.2971591 fatcat:7wak5tr6gfabpnx7vmail55mea

Automated Machine Learning in Practice: State of the Art and Recent Results

Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach, Stefan Lorwald, Anastasia Varlet, Christian Westermann, Thilo Stadelmann
2019 2019 6th Swiss Conference on Data Science (SDS)  
Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so.  ...  A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions  ...  ACKNOWLEDGEMENT We are grateful for support by Innosuisse grant 25948.1 PFES "Ada" and helpful discussions with Martin Jaggi.  ... 
doi:10.1109/sds.2019.00-11 dblp:conf/sds2/TuggenerARLVWS19 fatcat:okiclde7nrb3xgyatmlqjh4use

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning [article]

David Jacob Kedziora and Katarzyna Musial and Bogdan Gabrys
2022 arXiv   pre-print
In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level  ...  impact on selecting ML models/algorithms.  ...  Regardless, once a set of meta-features have been selected for a class of ML problems, with each problem generally corresponding to an inflow dataset, a standard practice for meta-learning is to construct  ... 
arXiv:2012.12600v2 fatcat:6rj4ubhcjncvddztjs7tql3itq

A survey on data‐efficient algorithms in big data era

Amina Adadi
2021 Journal of Big Data  
This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with  ...  In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms.  ...  Researchers should thus evaluate their algorithms on a diverse suite of data sets with different quantities of unlabeled data and report how performance varies with the amount of unlabeled data.  ... 
doi:10.1186/s40537-021-00419-9 fatcat:v4uahsvhlzdldlxqf24bshmja4

Application of Computational Intelligence to Energy Systems

Matteo De Felice
2011 Zenodo  
Neural Networks Structure for Forecasting Neural networks have been applied successfully to a wide variety of forecasting problems.  ...  An initial feature selection is performed among the input variables and MLP neural networks are compared with other data mining method in finding relationships between the steam load and the weather data  ...  Appendix A Computational Intelligence in Software Packages This appendix gives a list of some of the most common software implementations of neural networks and computational intelligence algorithms.  ... 
doi:10.5281/zenodo.4068383 fatcat:ee6uyhkcdzh3hlvty33twlqnva

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges [article]

Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera
2020 arXiv   pre-print
Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parametric configurations with  ...  , which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and c) challenges and new directions of research  ...  ), and the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the Red Cervera Programme (AI4ES project).  ... 
arXiv:2008.03620v1 fatcat:eklkgo7n35a2ngllgymarygjdi

Deep Reinforcement Learning [article]

Yuxi Li
2018 arXiv   pre-print
We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.  ...  Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn.  ...  The authors propose policy-space response oracle (PSRO), and its approximation, deep cognitive hierarchies (DCH), to compute best responses to a mixture of policies using deep RL, and to compute new meta-strategy  ... 
arXiv:1810.06339v1 fatcat:kp7atz5pdbeqta352e6b3nmuhy

Sports Analytics: Predicting Athletic Performance with a Genetic Algorithm

Victor Cordes, Lorne Olfman
2016 Americas Conference on Information Systems  
Feature subset selection by way of a genetic algorithm to identify and assess the combinatorial advantage for a group of metrics is a viable option to otherwise arbitrary model construction.  ...  The resulting dizzying ecosystem of choice is especially difficult to overcome and leaves a residual uncertainty regarding true strength of output, specifically for practical implementations.  ...  Various feature subsets are selected and input into the learning algorithm. Said subsets are processed through an accuracy evaluation metric and ranked accordingly.  ... 
dblp:conf/amcis/CordesO16 fatcat:rni3zpzsqjgnxkxgfltr6h4bhq

Artificial Intellgence – Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 [article]

Karl-Herbert Schäfer
2021 arXiv   pre-print
and management) and the University of Applied Sciences and Arts Northwestern Switzerland.  ...  Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others.  ...  Acknowledgements The authors would like to thank D. Iordanov for the contribution to the development of the application within his studies at Mainz University of Applied Sciences.  ... 
arXiv:2112.05657v1 fatcat:wdjgymicyrfybg5zth2dc2i3ni

An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods

Qing Zhao, Guohong Yao, Faheem Akhtar, Jianqiang Li, Yan Pei
2020 IEEE Access  
(c): Using Artificial neural network with various hidden layers and neurons with 100 ranked features using the mRMR feature selection scheme.  ...  Whereas, mRMR feature selection scheme and Artificial deep neural network (ANN) is also exploited for the comparative perspective.  ...  He is currently working as an Associate Professor with the University of Aizu. His research interests include evolutionary computation, machine learning, and software engineering.  ... 
doi:10.1109/access.2020.3039867 fatcat:banikhctibhohgqscpil6l37uu

A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

Bo Ma, Weisi Guo, Jie Zhang
2020 IEEE Access  
Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees.  ...  In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital  ...  Forecasting social behaviours requires different kinds of online data for modelling user activities and corresponding meta information.  ... 
doi:10.1109/access.2020.2975004 fatcat:ccl2trwgkrek5fmkorfwjesq6q
« Previous Showing results 1 — 15 out of 322 results