Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
C4.5: programs for machine learning
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Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
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Human motion analysis: a review
Computer Vision and Image Understanding
Constructing X-of-N Attributes for Decision Tree Learning
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Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Feature Extraction, Construction and Selection: A Data Mining Perspective
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Function decomposition in machine learning
Machine Learning and Its Applications
Data Mining and Knowledge Discovery with Evolutionary Algorithms
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Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Attribute Selection with a Multi-objective Genetic Algorithm
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
a CAPpella: programming by demonstration of context-aware applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Natural programming languages and environments
Communications of the ACM - End-user development: tools that empower users to create their own software solutions
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Evolutionary Constructive Induction
IEEE Transactions on Knowledge and Data Engineering
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
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Activity Recognition Based on Semi-supervised Learning
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
A review of feature selection techniques in bioinformatics
Bioinformatics
DARA: Data Summarisation with Feature Construction
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Searching for interacting features in subset selection
Intelligent Data Analysis
Easing the Smart Home: Translating Human Hierarchies to Intelligent Environments
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Feature Construction and Feature Selection in Presence of Attribute Interactions
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Evolutionary multi-feature construction for data reduction: A case study
Applied Soft Computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Constructive induction on decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A flexible sequence alignment approach on pattern mining and matching for human activity recognition
Expert Systems with Applications: An International Journal
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Managing Adaptive Versatile environments
Pervasive and Mobile Computing
Feature construction and selection using genetic programming and a genetic algorithm
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Managing pervasive environment privacy using the "fair trade" metaphor
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
Learning patterns in ambient intelligence environments: a survey
Artificial Intelligence Review
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
Environmental user-preference learning for smart homes: An autonomous approach
Journal of Ambient Intelligence and Smart Environments
Pattern Analysis & Applications
Rule-based contextual reasoning in ambient intelligence
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
Computing with instinct
Strengthening learning algorithms by feature discovery
Information Sciences: an International Journal
Temporal data mining for smart homes
Designing Smart Homes
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
Personal ambient intelligent reminder for people with cognitive disabilities
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
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One of the goals in Ambient Intelligence is to enable Intelligent Environments to take decisions based on the perceived context. In our previous work, we successfully explored how the inhabitants can communicate their own preferences with the environment using Event-Condition-Action ECA rules. The easiness of the communication language combined with an appropriate explanation mechanism gives trust to the Intelligent Environment actions. However, defining every preference, and maintaining them up-to-date can be cumbersome. Therefore, a complementary mechanism is required to learn from user behavior and adapt to small changes without being explicitly requested for. Inferring behaviors effectively from data collected from sensors in an Intelligent Environment is a challenging problem. The main issues include primitive representation of data, the necessity of a high number of sensors, and dealing with few training data collected in a short time. We present MFE3/GADR, an evolutionary constructive induction method to ease inferring inhabitants' preferences from data collected from simple sensors. We show that this method detects successfully relevant sensors and constructs highly informative features that abstract relations among them. The constructed features, in addition to improving significantly the learning accuracy, break down and encapsulate the performance of inhabitants into decision trees that can easily be converted to ECA rules for further use in the Intelligent Environment. Comparing the empirical results show that our method can reduce a large set of complex ECA rules that represent the preferences to a smaller set of simple ECA rules.