chapter 6. classification and prediction - rizal setya · pdf filechapter 6. classification...
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Chapter 6. Classification and Prediction
Eager Learners: when given a set of training tuples,
will construct a generalization model before receiving
new tuples to classify
Classification by decision tree induction
Rule-based classification
Classification by back propagation
Support Vector Machines (SVM)
Associative classification
Lazy vs. Eager Learning
Lazy vs. eager learning
Lazy learning (e.g., instance-based learning): Simply stores training data (or only minor processing) and waits until it is given a test tuple
Eager learning (the above discussed methods): Given a set of training set, constructs a classification model before receiving new (e.g., test) data to classify
Lazy: less time in training but more time in predicting
Lazy Learner: Instance-Based Methods
Typical approaches
k-nearest neighbor approach
Instances represented as points in a Euclidean space.
The k-Nearest Neighbor Algorithm
All instances correspond to points in the n-D space
The nearest neighbor are defined in terms of Euclidean distance, dist(X1, X2)
Target function could be discrete- or real- valued
For discrete-valued, k-NN returns the most common value among the k training examples nearest to xq
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The k-Nearest Neighbor Algorithm
k-NN for real-valued prediction for a given unknown tuple
Returns the mean values of the k nearest neighbors
Distance-weighted nearest neighbor algorithm
Weight the contribution of each of the k neighbors according to
their distance to the query xq
Give greater weight to closer neighbors
Robust to noisy data by averaging k-nearest neighbors
The k-Nearest Neighbor Algorithm
How can I determine the value of k, the number of neighbors?
In general, the larger the number of training tuples is, the larger the value of k is
Nearest-neighbor classifiers can be extremely slow when classifying test tuples O(n)
By simple presorting and arranging the stored tuples into search tree, the number of comparisons can be reduced to O(logN)
The k-Nearest Neighbor Algorithm
Example:
K=5
Distance Measure
Distance Measure Example
WINE CHERRY CHEWY
TANNINS
BEAUTY
WINE1 1 1 1
WINE2 0 0 1
What is the prediction if k=1? K=3? K=5?
WINE blueberry blackberry CHERRY CHEWY
TANNINS
Score
Wine1 1 0 0 1 90+
Wine2 1 1 1 0 90+
Wine3 0 1 0 1 90+
Wine4 0 1 1 1 90-
Wine5 0 0 1 0 90-
Wine6 0 0 1 1 90-
Unknown 1 1 0 1 ?