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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.