Optimization of Domain-Independent Classification Framework for Mood Classification


Sung-Pil Choi, Yuchul Jung, Sung-Hyon Myaeng, Journal of Information Processing Systems Vol. 3, No. 2, pp. 73-81, Apr. 2007


Keywords: Text Classification, Mood Categorization, Information Retrieval, Feature Selection, Text Classification Application
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Abstract

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 with the baseline.


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Cite this article
[APA Style]
Sung-Pil Choi, Yuchul Jung, & Sung-Hyon Myaeng (2007). Optimization of Domain-Independent Classification Framework for Mood Classification. Journal of Information Processing Systems, 3(2), 73-81. DOI: .

[IEEE Style]
S. Choi, Y. Jung and S. Myaeng, "Optimization of Domain-Independent Classification Framework for Mood Classification," Journal of Information Processing Systems, vol. 3, no. 2, pp. 73-81, 2007. DOI: .

[ACM Style]
Sung-Pil Choi, Yuchul Jung, and Sung-Hyon Myaeng. 2007. Optimization of Domain-Independent Classification Framework for Mood Classification. Journal of Information Processing Systems, 3, 2, (2007), 73-81. DOI: .