Region Analogy-Based Quantization Method for Numerical Data

Abstract:

Data quantization methods for numerical attributes play an extremely important role in handy talents statistics assessment and technology learning because discrete values of attributes are required in most classification methods. In this paper, we present an region analogy-based quantization method for numerical data. It expresses an inner coherence criterion which is thought to be as a new criterion in the ways of quantization. In addition, a heuristic quantization algorithm is proposed to achieve a satisfying quantization result with the aim to improve the performance of inductive learning algorithms. They realize criterion as well as quantifying the true value attributions exactly and reasonably. Strict experiments on UCI reflects data sets express that our supposed algorithm produces a superior quantization scheme that promotes the classification right of inductive learning than emerging quantization algorithms.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 1743-1746
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.1743
• Online since: July 2014
• Authors: Yu Di Sun*
• Keywords: Machine Learning, Quantization, Region Analogy

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