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J Model: A Simple and Runtime-Adaptive Ensemble Architecture for Classification

SSP Seminar Series
Jinhan Kim

Many ensemble methods have been applied to classification. Those have been used sophisticated and static approaches to construct an ensemble. In this paper, we suggest a new architecture that is simple and runtime-adaptive called J Model. It is based on the diversity of learning agents and their peer ranked voting. We are able to generate ensemble models that can be adjusted easily and give a good performance for the purposes of classification tasks. We present the details of J Model and empirical studies over the UCI benchmark datasets, and show that the proposed ensemble architecture is effective for classification.

Date and time: 
Tuesday, 18 November, 2008 - 15:00
60 minutes