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首页> 外文期刊>International journal of applied evolutionary computation >Statistical Discriminability Estimation for Pattern Classification Based on Neural Incremental Attribute Learning
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Statistical Discriminability Estimation for Pattern Classification Based on Neural Incremental Attribute Learning

机译：基于神经增量属性学习的模式分类统计可分辨性估计

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摘要

Feature ordering is a significant data preprocessing method in Incremental Attribute Learning (ML), a novel machine learning approach which gradually trains features according to a given order. Previous research has shown that, similar to feature selection, feature ordering is also important based on each feature's discrimination ability, and should be sorted in a descending order of their discrimination ability. However, such an ordering is crucial for the performance of ML. As the number of feature dimensions in ML is increasing, feature discrimination ability also should be calculated in the corresponding incremental way. Based on Single Discriminability (SD), where only the feature discrimination ability is computed a new filter statistical feature discrimination ability predictive metric, called the Accumulative Discriminability (AD), is designed for the dynamical feature discrimination ability estimation. Moreover, a criterion that summarizes all the produced values of AD is employed with a GA (Genetic Algorithm)-based approach to obtain the optimum feature ordering for classification problems based on neural networks by means of ML. Compared with the feature ordering obtained by other approaches, the method proposed in this paper exhibits better performance in the final classification results. Such a phenomenon indicates that, (ⅰ) the feature discrimination ability should be incrementally estimated in ML, and (ⅱ) the feature ordering derived by AD and its corresponding approaches are applicable with ML.
机译：特征排序是增量属性学习（ML）中的一种重要的数据预处理方法，它是一种新颖的机器学习方法，可以根据给定的顺序逐步训练特征。先前的研究表明，类似于特征选择，基于每个特征的判别能力，特征排序也很重要，并且应该按照其辨别能力的降序进行排序。但是，这种排序对于ML的性能至关重要。随着ML中特征维数的增加，特征判别能力也应以相应的增量方式进行计算。基于仅可分辨能力（SD）的情况，仅计算特征分辨能力，就设计了一种新的过滤器统计特征分辨能力预测指标，称为累积可分辨性（AD），用于动态特征分辨能力估计。此外，将总结所有AD产生值的标准与基于GA（遗传算法）的方法结合使用，以基于ML通过神经网络获得分类问题的最佳特征排序。与通过其他方法获得的特征排序相比，本文提出的方法在最终分类结果中表现出更好的性能。这种现象表明，（ⅰ）应该在ML中逐步估计特征辨别能力，并且（ⅱ）AD及其相应方法得出的特征排序适用于ML。

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