Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH), to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

UAV reconnaissance has the advantages of strong mobility and high positioning accuracy, and efficient multi-UAV cooperative operation can greatly improve the search and reconnaissance capability, which is of great significance to the collaborative perception of sea situation. The purpose of this paper is to study the problem of optimal route planning for multi-UAV collaborative search of maritime targets. The core method is to use the distributed model predictive control method integrating OODA theory to optimize the cooperative maritime reconnaissance model of multiple UAVs in the planning period, and then use distributed MPC to solve the cooperative maritime search problem of multiple UAVs. This paper innovatively uses the idea of OODA model, fits in with the modern battlefield environment, and greatly improves the efficiency of actual combat UAV search.

The patterns of different financial data sources vary substantially, and accordingly, investors exhibit heterogeneous cognition behavior in information processing. To capture different patterns, we propose a novel approach called the two-stage dynamic stacking ensemble model based on investor knowledge representations, which aims to effectively extract and integrate the features from multi-source financial data. In the first stage, we identify different financial data property from global stock market indices, industrial indices, and financial news based on the perspective of investors. And then, we design appropriate neural network architectures tailored to these properties to generate effective feature representations. Based on learned feature representations, we design multiple meta-classifiers and dynamically select the optimal one for each time window, enabling the model to effectively capture and learn the distinct patterns that emerge across different temporal periods. To evaluate the performance of the proposed model, we apply it to predicting the daily movement of Shanghai Securities Composite index, SZSE Component index and Growth Enterprise index in Chinese stock market. The experimental results demonstrate the effectiveness of our model in improving the prediction performance. In terms of accuracy metric, our approach outperforms the best competing models by 1.42%, 7.94%, and 7.73% on the SSEC, SZEC, and GEI indices, respectively. In addition, we design a trading strategy based on the proposed model. The economic results show that compared to the competing trading strategies, our strategy delivers a superior performance in terms of the accumulated return and Sharpe ratio.