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Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

Signal prediction has attracted more and more attention from data mining and machine learning communities. Decision stump is a one-level decision tree, and it classifies instances by sorting them based on feature values. The boosting is a kind of powerful ensemble method and can improve the performance of prediction significantly. In this paper, boosting and decision stump algorithm are combined to analyse and predict the signal data. An experimental evaluation is carried out on the public signal dataset and the experimental results show that the boosting and decision stump-based algorithm clearly improves performance of signal prediction.

Granular computing is a new conceptual and computing paradigm of information processing, the idea of which is the use of granules for problem solving at different granularities. Web page classification is an important research direction for Web mining. In this paper, we propose an approach to Web page classification based on granules. Some concepts are defined firstly. Then, a Web page classification framework based on granules is built and an automatic classification algorithm is proposed. Experiment results demonstrate that the proposed approach is promising and effective.1

As electronic commerce (EC) become more and more prevalent to people, it is very critical to provide the right information to the right customers. The EC sites are generating large amount of data on customer purchases. Data mining techniques in EC domain is currently a hot research area. This paper proposes a rough set based approach to obtaining patterns of goods and classifying associated goods into groups, which can be used by the sites to group their goods according to the customers' preference and adopt appropriate selling policies.