IEEE Trans Cybern. 2019 Feb;49(2):366-379. doi: 10.1109/TCYB.2017.2761908. Epub 2017 Oct 26.
High dimensional data classification with very limited labeled training data is a challenging task in the area of data mining. In order to tackle this task, we first propose a feature selection-based semi-supervised classifier ensemble framework (FSCE) to perform high dimensional data classification. Then, we design an adaptive semi-supervised classifier ensemble framework (ASCE) to improve the performance of FSCE. When compared with FSCE, ASCE is characterized by an adaptive feature selection process, an adaptive weighting process (AWP), and an auxiliary training set generation process (ATSGP). The adaptive feature selection process generates a set of compact subspaces based on the selected attributes obtained by the feature selection algorithms, while the AWP associates each basic semi-supervised classifier in the ensemble with a weight value. The ATSGP enlarges the training set with unlabeled samples. In addition, a set of nonparametric tests are adopted to compare multiple semi-supervised classifier ensemble (SSCE)approaches over different datasets. The experiments on 20 high dimensional real-world datasets show that: 1) the two adaptive processes in ASCE are useful for improving the performance of the SSCE approach and 2) ASCE works well on high dimensional datasets with very limited labeled training data, and outperforms most state-of-the-art SSCE approaches.
高维数据分类是数据挖掘领域中的一个具有挑战性的任务，特别是在训练数据非常有限的情况下。为了解决这个问题，我们首先提出了一种基于特征选择的半监督分类器集成框架（FSCE）来进行高维数据分类。然后，我们设计了一种自适应半监督分类器集成框架（ASCE）来提高 FSCE 的性能。与 FSCE 相比，ASCE 的特点是自适应特征选择过程、自适应加权过程（AWP）和辅助训练集生成过程（ATSGP）。自适应特征选择过程基于特征选择算法选择的属性生成一组紧凑的子空间，而 AWP 则为集成中的每个基本半监督分类器关联一个权重值。ATSGP 用未标记的样本扩充训练集。此外，还采用了一组非参数检验来比较不同数据集上的多种半监督分类器集成（SSCE）方法。在 20 个高维真实数据集上的实验表明：1）ASCE 中的两个自适应过程有助于提高 SSCE 方法的性能，2）ASCE 适用于具有非常有限的标记训练数据的高维数据集，并且优于大多数最新的 SSCE 方法。
