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JIPS
2007

Optimization of Domain-Independent Classification Framework for Mood Classification

13 years 10 months ago
Optimization of Domain-Independent Classification Framework for Mood Classification
In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naïve Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared to the baseline.
Sung-Pil Choi, Yuchul Jung, Sung-Hyon Myaeng
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JIPS
Authors Sung-Pil Choi, Yuchul Jung, Sung-Hyon Myaeng
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