Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Creating an Ambient-Intelligence Environment Using Embedded Agents
IEEE Intelligent Systems
The Journal of Machine Learning Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
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In Internet of Things, softwares shall enable their host objects (everyday-objects) to monitor other objects, take actions, and notify humans while using some form of reasoning. The ever changing nature of real life environment necessitates the need for these objects to be able to generalize various inputs inductively in order to play their roles more effectively. These objects shall learn from stored training examples using some generalization algorithm. In this paper, we investigate training sets requirements for object learning and propose a Stratified Ordered Selection (SOS) method as a means to scale down training sets. SOS uses a new instance ranking scheme called LO ranking. Everyday-objects use SOS to select training subsets based on their capacity (e.g. memory, CPU). LO ranking has been designed to broaden class representation, achieve significant reduction while offering same or near same analytical results and to facilitate faster on-demand subset selection and retrieval for resource constrained objects. We show how SOS outperforms other methods using well known machine learning datasets.