Paper Info
Title
On-demand Data Numerosity Reduction for Learning Artifacts
Abstract
In domains in which single agent learning is a more natural metaphor for an artifact-embedded agent, Exemplar-Based Learning (EBL) requires significantly large sets of training examples for it to be applicable. Obviously large sets of training examples contradict resource capabilities of artifacts. To make EBL a possibility for these artifacts, sets of training examples must be reduced in size in a way that does not compromise learning performance in order to relieve artifacts' resources (e.g. memory). In this paper, we investigate training sets requirements for artifacts learning and propose a ranking-based Stratified Ordered Selection (SOS) method to scale them down. Contrary to reduction approaches in mainstream learning, this method has been designed with resource constraint nature of artifacts in mind. Artifacts shall use an intermediary which implements SOS to, dynamically and on-demand, retrieve training subsets based on their resource capacities (e.g. memory, CPU). SOS uses a new Level Order (LO) ranking scheme which has been designed to broaden representation of classes of examples, to quicken data retrieval, and to allow for retrieval of subsets of varying sizes while ensuring same or near same learning performance. We present how SOS evaluates on various well known machine learning datasets and how it compares to some of the best performing data reduction approaches.
Year
DOI
Venue
2012
10.1109/AINA.2012.108
AINA
Keywords
Field
DocType
training example,resource capability,learning artifacts,single agent learning,large set,learning performance,resource capacity,data reduction approach,artifact-embedded agent,mainstream learning,resource constraint nature,on-demand data numerosity reduction,artificial intelligent,training data,multi agent systems,learning artificial intelligence,machine learning,artificial intelligence,binary tree,binary trees,data retrieval,reliability,data reduction,accuracy
Data mining,Numerosity adaptation effect,On demand,Ranking,Data retrieval,Computer science,Binary tree,Multi-agent system,Artificial intelligence,Machine learning,Metaphor,Data reduction
Conference
ISSN
Citations 
PageRank 
1550-445X
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Khamisi Kalegele194.68
Hideyuki Takahashi2142.80
Johan Sveholm352.48
Kazuto Sasai4169.64
Gen Kitagata56821.66
Tetsuo Kinoshita634385.41