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

3.4 Semi-supervised Learning 57While Unlabeled data U ̸=/0 doTrain the classifier c based on L;Use c to classify the data in U;X=/0;For each data d ∈ U doIf d.con f ident ≥ t then X=X ∪ d;endL=L+X;U=U-X;endIn a brief summary, the advantages of the self-training approach are: (1) the method isintuitive and simple can be wrapped to other more complex classifiers; and (2) it can be appliedto many real applications, i.e., word sense disambiguation [265]. The disadvantage of the selftrainingis that the early error could be accumulated and enlarged in the later procedures.3.4.2 Co-TrainingBlum et al. [37] first proposed the co-training algorithm based on the idea of self-trainingby employing two classifiers. In the paper, the authors have made such assumptions that thefeatures of data can be partitioned into two independent subsets (w.r.t class) and each subsetcan be used to train a good classifier. The process of the algorithm thus can be fulfilled as adual training of the two classifiers to each other.The pseudo code of self-training is shown in Algorithm 3.11 [53].Algorithm 3.11: The co-training algorithmInput: A labeled dataset L, an unlabeled dataset U, a confident threshold tOutput: All data with labeled classSplit features into two sets, F 1 and F 2 ;Initial two classifiers c 1 and c 2 ;For ach data d ∈ L doProject d to the two feature sets, F 1 and F 2 , resulting in d 1 and d 2 ;Train c 1 based on d 1 and c 2 based on d 2 ;endWhile Unlabeled data U ̸=/0 do// Train c 2 by c 1 ;Use c 1 to classify U in the feature space F 1 ;X 1 =/0;For each data d ∈ U doIf d.con f ident ≥ t then X 1 =X 1 ∪ d ;endd 2 =d 2 ∪ X 1 ;// Train c 1 by c 2 ;Use c 2 to classify U in the feature space F 2 ;X 2 =/0;For each data d ∈ U doIf d.con f ident ≥ t then X 2 =X 2 ∪ d;