The document summarizes different machine learning classification techniques including instance-based approaches, ensemble approaches, co-training approaches, and partially supervised approaches. It discusses k-nearest neighbor classification and how it works. It also explains bagging, boosting, and AdaBoost ensemble methods. Co-training uses two independent views to label unlabeled data. Partially supervised approaches can build classifiers using only positive and unlabeled data.

7. Definition of Nearest Neighbor K-nearest neighbors of a record x are data points that have the k smallest distance to x

20. General Idea

23. Bagging Algorithm Let k be the number of bootstrap samples set For i =1 to k do Create a bootstrap sample D i of Size N Train a (base) classifier C i on D i End for

26. Boosting C 1 T D 1 F (D 2 ) C 2 T D m … C m T The process of generating classifiers F

29. AdaBoosting Algorithm The weight update mechanism (Equation): where is the normalization factor: : the weight for example ( x i , y i ) during the round

30. AdaBoosting Algorithm Let k be the number of boosting rounds, D is the set of all examples Update the weight of each examples according to Equation End for , Initialize the weights for all N examples For i = 1 to k do Create training set D i by sampling from D according to W . Train a base classifier C i on D i Apply C i to all examples in the original set D

35. Co-Training Approach Feature Set X=(X1, X2) Classification Model One Classification Model Two new labeled data set 1 subset X1 subset X2 training training new labeled data set 2 classifying classifying Unlabeled data Unlabeled data example set L example set L

37. Co-training algorithm For instance, p=1, n=3, k=30, and u=75

42. Positive und Unlabeled

45. Two Steps Process

46. Step 1 Step 2 positive negative Reliable Negative (RN) Q =U - RN U P positive Using P, RN and Q to build the final classifier iteratively or Using only P and RN to build a classifier Existing 2-step strategy