Proceedings ArticleDOI
Learning in the Presence of Self-Interested Agents
Haldun Aytug,F. Boylu,Gary J. Koehler +2 more
- Vol. 7, pp 158
TLDR
The need for a paradigm for anticipating this kind of strategic behavior inherent in the sample data generation process is studied and related research issues are outlined.Abstract:
In many situations a principal gathers a data sample containing positive and negative examples of a concept to induce a classification rule using a machine learning algorithm. Although learning algorithms differ from each other in various aspects, there is one essential issue that is common to all: the assumption that there is no strategic behavior inherent in the sample data generation process. In that respect, we ask the question "what if the observed attributes are being deliberately modified by the acts of some self-interested agents who will gain a preferred classification by engaging in such behavior". Therein such cases, there is a need for anticipating this kind of strategic behavior and incorporating it into the learning process. Classical learning approaches do not consider the existence of such behavior. In this paper we study the need for this kind of a paradigm and outline related research issues.read more
Citations
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Induction over constrained strategic agents
TL;DR: This paper explores Induction over Strategic Agents for a class of problems where attributes are binary values and finds that binary valued attributes can be modified by agents wishing to achieve a positive classification.
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Enhanced ontology-based text classification algorithm for structurally organized documents
TL;DR: The objectives of this thesis are to formulate algorithms for classifying text by creating suitable feature vector and reducing the dimension of data which will enhance the classification accuracy.
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