COSC 6335
Homework3 Fall 2008
Dr. Eick
Due: Thursday, December 4 in class (only problem 8 and 11)
7) Clustering [8] Ungraded!! a) DBSCAN and DENCLUE both compute outliers. How do they define outliers? What are the main differences in their procedures to determine outliers? [3] b) What role does the parameter

play in DENCLUE’s density estimation approach? If we choose a very small value for

; what is the impact on the density function? [2] c) How is subspace clustering different from traditional clustering? [3]
8) Ensemble Methods [7] a) What is the key idea of ensemble methods? Why have they been successfully used in many applications to obtain classifiers with high accuracies? [2] b) In which case, does AdaBoost increase the weight of an example? Why does the
AdaBoost algorithm modify the weights of examples? [3] c) What role does the parameter
 i
play in the AdaBoost algorithm? [2]
9) Preprocessing [6] Ungraded!! a) Assume you have to do feature selection for a classification task. What are the characteristics of features (attributes) you might remove from the dataset prior to learning the classification algorithm? [3] b) Give an example of a problem that might benefit from feature creation [3].
10) ROC Curves [6] Ungraded!!
a) Create the ROC-curve for the following classifier based on the results of applying a support vector machine classifier
1
to the following 4 examples [4]:
Example# Correct Class
1
2
3
4
Classifier-
Output
-0.25
-0.02
+0.03
+0.23
Selected
Class


+
+


+
+ b) Interpret the ROC curve! Does the curve suggest that the classifier is a good classifier?
[2]
11) Nearest Neighbor Classifiers and Support-Vector Machines [8] a) Why do most support vector machine machines map examples to a higher dimensional space? Support vector machine maximize margins when creating hyperplanes. What is the motivation for doing that? Why a large margins desirable? [4] b) Assume you want to use a nearest neighbor classifier for a particular classification task. How would you approach the problem to choosing the parameter k of a nearest neighbor classifier? [2] c) Why are nearest neighbor classifiers called lazy classifiers ? [2]
1 We assume that the classifier assigns class ‘+’ to an example if the returned value is greater 0, and class ‘-
‘, otherwise.
1