Titlebook: Machine Learning and Principles and Practice of Knowledge Discovery in Databases; International Worksh Irena Koprinska,Paolo Mignone,Sepide

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Intrusion Detection Using Ensemble Models use pairs of strong and weak learners based on five different classifiers and combine them using weights derived through a Particle Swarm Optimization algorithm. We propose a voting and a stacking scheme to obtain the final predictions. We show the overwhelming advantage of using our proposed stack

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Domain Adaptation with Maximum Margin Criterion with Application to Network Traffic Classificationabeled samples from the desired network; In other words, we adapt shared applications while preserving the information about non-shared applications. In order to demonstrate the efficacy of our method, we construct five different cross-network datasets using the Brazil dataset. These results indicat

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Towards a General Model for Intrusion Detection: An Exploratory Studyme them. Then, we perform an experimental evaluation using several binary ML classifiers and a total of 16 feature learners on 4 public attack datasets. Results show that a model learned on a dataset or a system does not generalize well as is to other datasets or systems, showing poor detection perf

CODA

Domestic Hot Water Forecasting for Individual Housing with Deep Learning achieved satisfying performances in term of MSE on an individual residence dataset, showing that this approach is promising to conceive building energy management systems based on deep forecasting models.