基于多种函数拟合的自适应最优分割法

Adaptive Optimal Partition Method Based on Multi-function Fitting

对于有特定发展趋势和特征的数据样本,Fisher最优分割法仅考虑样本组内方差,忽视了其特定变化规律函数,分割结果有时差强人意。文章提出了一种基于多种函数拟合的自适应最优分割法,对具有发展规律的数据样本可以很好地找到其分割点,并直观给出其特征函数。该方法增加了对数据样本分割的适应性,提升了对复杂数据的分类准确性,且为函数分段拟合提供了具体方法。

For data samples with specific development trends and features, Fisher’s optimal partition method only considers the variance within sample groups and ignores its specific variation rule function, sometimes with the segmentation results unsatisfactory. This paper proposes an adaptive optimal partition method based on multi-function fitting, which can locate the partitioning point well and intuitively presents the characteristic functions. This method improves the adaptability of data sets partitioning, enhances the classification accuracy of sophisticated data, and provides a specific method for function segmentation fitting.