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.

• Type: Journal
• Year: 2007
• Where: JIPS
• Authors: Sung-Pil Choi, Yuchul Jung, Sung-Hyon Myaeng
• Keywords: Domain-independent Classification Framework, Enhanced Classification Performance, JIPS 2007, Naive Bayesian Classification