Feature selection is a popular technique of preprocessing data. In order to deal with dynamic or large data, incremental feature selection has been developed, in which the features selected from existing data are integrated with those mined from both existing and dynamic data in the manner of incremental computation. Fuzzy rough set theory is powerful in handling uncertainty in real-valued data or even mixed data, and one of its most important applications is feature selection. Nevertheless, not much work has been found on fuzzy-rough-set-based incremental feature selection. Therefore, in this article, we investigate the incremental feature selection using a fuzzy-rough-set-based information entropy with incoming instances. Specifically, the representative instances from the incoming ones are first selected according to the information coverage of fuzzy granules generated by fuzzy rough sets. Then, the incremental mechanism of the fuzzy-rough-set-based information entropy is formulated by adding newcome instances. Finally, an incremental feature selection procedure, which we call active incremental feature selection, is proposed. Furthermore, some numerical experiments are conducted to assess the performance of the proposed feature selection algorithm, and the results show that our algorithm is of a prominent advantage in terms of computational time, especially for a dataset with large number of instances.

Data reduction, aiming to reduce the original data by selecting the most representative information, is an important technique of preprocessing data. At present, large-scale or huge data are very common and the development of data reduction techniques for such data has attracted much attention. As a powerful tool for handling uncertainty in real-valued data, the fuzzy rough set theory has been widely applied to data reduction including extensive feature selection methods and some instance selection approaches. Nevertheless, not much work has been devoted to the simultaneous selection of feature and instance based on fuzzy rough sets. In this paper, we investigate the fuzzy rough set-based bi-selection issue for data reduction. Specifically, the unified concepts of the importance degrees of fuzzy granules are presented to select the representative instances first and then the critical features. An instance selection algorithm with a noise elimination technique is provided to firstly remove the noise and then select the representative instances according to the importance degrees of fuzzy granules. Then, the importance-degree-preserved attribute reduction is proposed, and a corresponding feature selection algorithm with a wrapper technique is given to search for a best feature subset. Lastly, the bi-selection method based on fuzzy rough sets (BSFRS) is presented for data reduction by integrating the instance selection and the feature selection methods. Moreover, some numerical experiments are conducted to assess the performance of BSFRS, and the results show that BSFRS performs well in terms of the effectiveness.

Concept-cognitive learning is an emerging area of cognitive computing, which refers to continuously learning new knowledge by imitating the human cognition process. However, the existing research on concept-cognitive learning is still at the level of complete cognition as well as cognitive operators, which is far from the real cognition process. Meanwhile, the current classification algorithms based on concept-cognitive learning models (CCLMs) are not mature enough yet since their cognitive results highly depend on the cognition order of attributes. To address the above problems, this article presents a novel concept-cognitive learning method, namely, stochastic incremental incomplete concept-cognitive learning method (SI2CCLM), whose cognition process adopts a stochastic strategy that is independent of the order of attributes. Moreover, a new classification algorithm based on SI2CCLM is developed, and the analysis of the parameters and convergence of the algorithm is made. Finally, we show the cognitive effectiveness of SI2CCLM by comparing it with other concept-cognitive learning methods. In addition, the average accuracy of our model on 24 datasets is 82.02%, which is higher than the compared 20 classification algorithms, and the elapsed time of our model also has advantages.