Intelligent assistance for data pre-processing

Bilalli, Besim; Abelló, Alberto; Aluja‐Banet, Tomàs; Wrembel, Robert

doi:10.1016/j.csi.2017.05.004

Cited by 41 publications

(34 citation statements)

References 19 publications

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“…Nguyen et al (2014) construct new pipelines using a beam search focussed on components recommended by a metalearner, and is itself trained on examples of successful prior pipelines. Bilalli et al (2018) predict which pre-processing techniques are recommended for a given classification algorithm. They build a meta-model per target classification algorithm that, given the t new meta-features, predicts which preprocessing technique should be included in the pipeline.…”

Section: Pipeline Synthesismentioning

confidence: 99%

Meta-Learning

Vanschoren¹

2019

The Springer Series on Challenges in Machine Learning

332

166

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Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

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Section: Pipeline Synthesismentioning

confidence: 99%

Meta-Learning

Vanschoren¹

2019

The Springer Series on Challenges in Machine Learning

332

166

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“…A significant amount of services that run on the QMP and Guifi.net network are network-intensive (i.e., bandwidth and delay sensitive), transferring large amounts of data between the network nodes [8,30]. The performance of such kind of services depends not just on computational and disk resources but also on the network bandwidth between the nodes on which they are deployed.…”

Section: Bandwidth Characterizationmentioning

confidence: 99%

“…PeerStreamer, 8 an open source live P2P video streaming service, has been paradigmatically established as the live streaming service in Cloudy. This service is based on chunk diffusion, where peers offer a selection of the chunks they own to some peers in their neighborhood.…”

Section: Live-video Streaming Servicementioning

confidence: 99%

A Lightweight Service Placement Approach for Community Network Micro-Clouds

et al. 2018

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Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, micro-clouds pursue not only

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“…When data analyst wants to do an analysis of electricity load consumption and pricing trends, then takes dataset of specific electricity company and performs some statistical analysis to get meaningful information. To keep balance between consumption and generation, many researchers are working on electricity load and price forecasting [4]. There are three types of forecasting: Short-Term Load Forecasting (STLF), Medium-Term Load Forecasting (MTLF) and Long-Term Load Forecasting (LTLF).…”

Section: Introductionmentioning

confidence: 99%

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

et al. 2019

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Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

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Intelligent assistance for data pre-processing

Cited by 41 publications

References 19 publications

Meta-Learning

Meta-Learning

A Lightweight Service Placement Approach for Community Network Micro-Clouds

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

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