关键词: 量化, 最小描述长度, 组合与概率, 权衡标准, 动态规划

Abstract: Quantization methods of continuous features are a necessary preprocess of data mining methods. This paper presents a trade-off discrimination method for continuous features based on minimum description length, combination and probability theories. It proposes a quantizative trade-off criterion for continuous features which reasonably balances classification errors and interval information generated by quantization. It proposes an effective dynamic programming quantization algorithm with the aim to find the best quantization result based on the trade-off criteria. The quantized data will be sent to naive bayes classifier to establish classification and prediction model. Contrastive experimental results show that the new method achieves higher mean learning accuracy than other quantization methods.

Key words: quantization, Minimum Description Length（MDL）, combination and probability, trade-off criteria, Dynamic Programming（DP）