On Classifying Drifting Concepts in P2P Networks Authors: Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng and Steven Hoi Keywords: Concept drift, classification, peer-to-peer (P2P) networks, distributed classification
Abstract:
Concept drift is a common challenge for many real-world data mining and knowledge discovery applications. Most of the existing studies for concept drift are based on centralized settings, and are often hard to adapt in a distributed computing environment. In this paper, we investigate a new research problem, P2P concept drift detection, which aims to effectively classify drifting concepts in P2P networks. We propose a novel P2P learning framework for concept drift classification, which includes both reactive and proactive approaches to classify the drifting concepts in a distributed manner. Our empirical study shows that the proposed technique is able to effectively detect the drifting concepts and improve the classification performance.
The authors define the problem as:
Concept drift refers to the learning problem where the target concept to be predicted, changes over time in some unforeseen behaviors. It is commonly found in many dynamic environments, such as data streams, P2P systems, etc. Real-world examples include network intrusion detection, spam detection, fraud detection, epidemiological, and climate or demographic data, etc.
The authors may well have been the first to formulate this problem among mechanical peers but any humanist could have pointed out examples concept drift between people. Both in the literature as well as real life.
Questions:
- What are the implications of concept drift for Linked Data? (3-5 pages, no citations)
- What are the implications of concept drift for static ontologies? (3-5 pages, no citations)
- Is concept development (over time) another form of concept drift? (3-5 pages, citations, illustrations, presentation)
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PS: Finding this paper is an illustration of ambiguity leading to serendipitous discovery. I searched for one of the author’s instead of the exact title of another paper. While scanning the search results I found this paper.