## 1 Introduction  
Various multi-dimensional heterogeneous data, such as low signal-to-noise ratio, can be collected through simultaneous access to information sources and used to comprehensively solve classification problems. For example, in gene expression [1] data, each sample may contain the expression levels of thousands of genes, which will be used as features for disease diagnosis, drug-response classification prediction, and other studies. In financial analysis [2], the basic features of each stock include opening price, closing price, high price, low price, volume, etc., and also have features such as macroeconomic indicators, company fundamental information, etc., which are used to construct complex stock market classification prediction models. Multiple data such as physiological signal data (heart rate, blood pressure, blood glucose level), electronic health records, diagnostic records, medication usage records, etc., are involved in the health and disease screening task [3], and data such as air quality sensor data, water quality monitoring data, meteorological data, and satellite remote sensing data are acquired through multiple sensors in an environmental monitoring [4] classification task. How to fully extract valid and interpretable information from these high-dimensional data has become a hot topic widely studied by scholars nowadays. Another issue is too much redundant information and the insufficient extraction of crucial information. This kind of high dimensional data with different independence and relevance increases the difficulty of information search and model matching and leads to poor classification results. Ensemble learning [5; 6; 7] algorithms can combine a variety of typical conventional classifiers organically and overcome the limitations of traditional single classifiers for high-dimensional-data classification tasks. The overall ensemble model is built by fully using various valid information regarding high-dimensional data. It can improve the robustness and generalization performance of the learning model. Many ensemble learning algorithms have been proposed in recent years, such as bagging [8], random forest [9], a fuzzy classifier ensemble [10; 11], and a neural network-based classifier ensemble [12; 13]. However, most ensemble algorithms are implemented directly on the original feature space, which must be revised in classification tasks for high-dimensional data with redundancy and noise. For sample space-based Adaboost algorithms [14], they do not provide substantial diversity for high-dimensional data containing redundancy; for feature space-based methods, such as random subspace algorithms [15], they do not provide effective filtering for high-dimensional features, and fusion on subspaces can lead to information loss and degrade classifier performance. Therefore, for high-dimensional data, although traditional integrated learning algorithms can accomplish the data classification task better, they still have the following shortcomings: 1. Simple math-driven methods make it difficult to compress the high-dimensional-data space, which makes it challenging to construct an accurate and reliable classification model. 2. Considering feature selection and model integration separately and independently will lead to the low robustness and scalability of the task of high-dimensional data. In order to overcome the above limitations, we propose a high-dimensional ensemble learning classification algorithm based on feature space reconstruction, called HDELC algorithm (HDELC is the abbreviation of high-dimensional ensemble learning classification), which selects and combines features more intelligently through feature space reconstruction to reduce dimensionality effectively instead of applying simple mathematical compression techniques on high-dimensional data, and retain the information crucial to the classification task. It focuses on data feature space and model integration, which can improve the diversity of models and accuracy in learning high-dimensional data to a certain extent.