16555

16555

يادگيري رده بندهاي پايدار مبتني بر درخت تصميم براي داده هاي جرياني

دكتري

هوش مصنوعي و رباتيك

شهريور 1395

دكتر محمدرضا كنگاوري

كامپيوتر

1395/11/18

1/1/1900 12:00:00 AM

Abstract: In this thesis, we have investigated the instability issue in decision tree learning algorithms an​d the causes of it. We have proposed a general abstract decision tree learning algorithm to induce more stable decision trees in comparison with traditional decision trees. In addition, we have proposed detailed algorithms including two batch algorithms to learn decision trees from static data an​d one incremental algorithm to learn decision trees from stream data. As there was no definition for the stability in the stream context, in this thesis, we have illustrated the working space by resolving the confusions in defining the stability for incremental decision tree learning algorithms. Although several references have declared that there is strong instability in the batch decision tree learning algorithms, but this issue has not been investigated for the incremental learning algorithms in the stream context. In this thesis, we have illustrated the presence of the instability issue in the incremental decision tree learning algorithms, theoretically an​d experimentally. To improve structural stability, i. e. to minimize the sensitivity of the decision tree structure to the training instances has been our focus in this thesis. The key solution of this thesis to improve the structural stability of decision trees, is to use non-monolithic split tests based on multiple attributes, designed with the aim of eliminating the competition between the attributes with close merits, localizing the effect of the training instances on the split test and, making the split test trainable. In the proposed algorithms of this thesis, the fuzzy min-max neural networks are employed as the split tests, in a way that provides the desired attributes. In the stream context, the proposed algorithm (which also had contributions in adapting Min-Max neural networks with concept drift) not only presents a good balance between stability an​d flexibility, but also provides advantages such as efficient adaptability with concept drift an​d new emerging classes. Theoretical analysis an​d experimental evidence show that the the proposed algorithms not only provide higher structural stability in comparison with available decision tree learning algorithms, but also create smaller an​d shallower models because of non-linearly splitting the feature space at the internal nodes an​d in the meanwhile, they present comparable precision an​d efficiency. Keywords: Decision tree, instability, Data Stream, Fuzzy min-max neural network, Concept drift, Classification.