Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

58 3 Algorithms and Techniquesendd 1 =d 1 ∪ X 2 ;Train c 1 based on d 1 and c 2 based on d 2 ;U=U-X 1 -X 2 ;endThe advantages of the co-training approach are: (1) the method is simple and based onself-training, and it can be wrapped to other more complex classifiers; and (2) it is not sosensitive to the early error compared with self-training. The disadvantage of the co-trainingis that the assumption seems to be too strong that the features of a dataset can not always bepartitioned independently into two subsets.3.4.3 Generative ModelsThe key idea of generative model based on semi-supervised learning approach is that, we constructan appropriate model which can best “match” the data and as a result, the classificationcan be easily done. The essential part for constructing the model is to find the proper valuesof the model parameters.There is an assumption that both the labeled and the unlabeled datasets have the samekind of model with similar parameters. One common strategy used is to employ EM-like (i.e.,expectation maximization [72]) algorithm to estimate the most like values of the parameters.Generally, the model to be chosen is application oriented and can be different accordingto different users, e.g., mixture of Gaussian, naive Bayes, and so forth. Algorithm 3.12 showsan example pseudo code of combining naive Bayes model and EM procedure [192, 53].Algorithm 3.12: The generative model based algorithmInput: A labeled dataset L, an unlabeled dataset UOutpot: All data with labeled classBuild the initial model M based on L;For i=0,1,2,... while results are not satisfactory doFor each data d ∈ U doE-step: estimate Pr(c|d,M) using a naive Bayes classifier, where c is a cluster;endFor each cluster c doM-step: estimate θ c to build the next model M i+1 , where θ is the parameter vector;endendThe advantages of the generative model based approach are: (1) the method is clear andembedded in a probabilistic framework; and (2) is very effective if the model is correct tomatch the data. The main drawbacks of the co-training are: (1) the appropriate model is difficultto choose and a wrong model may weaken the effectiveness of the approach; (and 2) EMis prone to local maxima. If global maximum is very different from local maximum, then theeffectiveness of the method is weakened.