Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance

Armano, Giuliano; Giuliani, Alessandro; Neumann, Ursula; Rothe, Nikolas; Heider, Dominik

doi:10.3390/make1010007

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…To make it easier to use, both a procedural version and an object-oriented version were made available as APIs. Further information on this aspect can be found in Armano et al (2019) [29].…”

Section: Few Details On the Algorithm Used For Training The Machine Learning Modelsmentioning

confidence: 99%

Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

Armano

Pegoraro

2022

Energies

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The design of new monitoring systems for intelligent distribution networks often requires both real-time measurements and pseudomeasurements to be processed. The former are obtained from smart meters, phasor measurement units and smart electronic devices, whereas the latter are predicted using appropriate algorithms—with the typical objective of forecasting the behaviour of power loads and generators. However, depending on the technique used for data encoding, the attempt at making predictions over a period of several days may trigger problems related to the high number of features. To contrast this issue, feature importance analysis becomes a tool of primary importance. This article is aimed at illustrating a technique devised to investigate the importance of features on data deemed relevant for predicting the next hour demand of aggregated, medium-voltage electrical loads. The same technique allows us to inspect the hidden layers of multilayer perceptrons entrusted with making the predictions, since, ultimately, the content of any hidden layer can be seen as an alternative encoding of the input data. The possibility of inspecting hidden layers can give wide support to researchers in a number of relevant tasks, including the appraisal of the generalisation capability reached by a multilayer perceptron and the identification of neurons not relevant for the prediction task.

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Section: Few Details On the Algorithm Used For Training The Machine Learning Modelsmentioning

confidence: 99%

Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

Armano

Pegoraro

2022

Energies

Self Cite

View full text Add to dashboard Cite

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Deep Learning on Chaos Game Representation for Proteins

Loechel

Eger

Sperlea

et al. 2019

Preprint

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Classi cation of protein sequences is one big task in bioinformatics and has many applications. Di erent machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF), and neural networks (NN). All of these methods have in common that protein sequences have to be made machinereadable and comparable in the rst step, for which di erent encodings exist. ese encodings are typically based on physical or chemical properties of the sequence. However, due to the outstanding performance of deep neural networks (DNN) on image recognition, we used frequency matrix chaos game representation (FCGR) for encoding of protein sequences into images. In this study, we compare the performance of SVMs, RFs, and DNNs, trained on FCGR encoded protein sequences. While the original chaos game representation (CGR) has been used mainly for genome sequence encoding and classi cation, we modi ed it to work also for protein sequences, resulting in n-akes representation, an image with several icosagons. We could show that all applied machine learning techniques (RF, SVM, and DNN) show promising results compared to the state-of-the-art methods on our benchmark datasets, with DNNs outperforming the other methods and that FCGR is a promising new encoding method for protein sequences.

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Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance

Cited by 2 publications

References 24 publications

Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

Deep Learning on Chaos Game Representation for Proteins

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