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.