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A generalized version space learning algorithm for noisy and uncertain data

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2 Author(s)
Tzung-Pei Hong ; Dept. of Inf. Manage., Kaohsiung Polytech. Inst., Taiwan ; Shian-Shyong Tsang

This paper generalizes the learning strategy of version space to manage noisy and uncertain training data. A new learning algorithm is proposed that consists of two main phases: searching and pruning. The searching phase generates and collects possible candidates into a large set; the pruning then prunes this set according to various criteria to find a maximally consistent version space. When the training instances cannot completely be classified, the proposed learning algorithm can make a trade-off between including positive training instances and excluding negative ones according to the requirements of different application domains. Furthermore, suitable pruning parameters are chosen according to a given time limit, so the algorithm can also make a trade-off between time complexity and accuracy. The proposed learning algorithm is then a flexible and efficient induction method that makes the version space learning strategy more practical

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Knowledge and Data Engineering, IEEE Transactions on  (Volume:9 ,  Issue: 2 )