Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Machine Learning
On growing better decision trees from data
On growing better decision trees from data
Supervised learning of an abstract context model for an intelligent environment
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Journal of Ambient Intelligence and Smart Environments
Learning patterns in ambient intelligence environments: a survey
Artificial Intelligence Review
Ambient intelligence: A survey
ACM Computing Surveys (CSUR)
Journal of Ambient Intelligence and Smart Environments
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This chapter aims to illustrate a possible way of using decision trees to make Smart Homes smarter. Decision trees are popular modelling technique, and the corresponding models are both predictive and descriptive. We formulate the modelling problem by defining the generic question “Is the undergoing activity or event in the Smart Home usual?” Then we explain how it is possible to gather appropriate data from the sensors and pre-process these data to form appropriate input for a decision tree algorithm. We further explain the mainstream approaches in decision trees algorithms rather then analysing them in detail, and we give short overview of available software. Finally, we explain some measures for quantitative and qualitative evaluation of the induced decision tree models (e.g. expert opinion, cross-validation, statistical tests etc.).