CISC 4631 Data Mining
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1 CISC 4631 Data Mining Lecture 03: Introduction to classification Linear classifier Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook authors) Eamonn Koegh (UC Riverside) 1
2 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. 2
3 10 10 Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K? 12 Yes Medium 80K? 13 Yes Large 110K? 14 No Small 95K? 15 No Large 67K? Test Set Induction Deduction Learning algorithm Learn Model Apply Model Model 3
4 Examples of Classification Task Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil Categorizing news stories as finance, weather, entertainment, sports, etc 4
5 Classification Techniques Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines We will start with a simple linear classifier 5
6 The Classification Problem (informal definition) Katydids Given a collection of annotated data. In this case 5 instances Katydids of and five of Grasshoppers, decide what type of insect the unlabeled example is. Grasshoppers Katydid or Grasshopper? 6
7 Color {Green, Brown, Gray, Other} For any domain of interest, we can measure features Has Wings? Abdomen Length Thorax Length Antennae Length Mandible Size Spiracle Diameter Leg Length 7
8 We can store features in a database. The classification problem can now be expressed as: Given a training database (My_Collection), predict the class label of a previously unseen instance Insect ID My_Collection Abdomen Length Antennae Length Insect Class Grasshopper Katydid Grasshopper Grasshopper Katydid Grasshopper Katydid Grasshopper Katydid Katydids previously unseen instance = ??????? 8
9 Grasshoppers Antenna Length Katydids Abdomen Length 9
10 Grasshoppers Antenna Length Katydids Abdomen Length Each of these data objects are called exemplars (training) examples instances tuples 10
11 We will return to the previous slide in two minutes. In the meantime, we are going to play a quick game. 11
12 Problem 1 Examples of class A Examples of class B
13 Problem 1 Examples of class A Examples of class B What class is this object? What about this one, A or B?
14 Problem 2 Oh! This ones hard! Examples of class A Examples of class B
15 Problem 3 Examples of class A Examples of class B This one is really hard! What is this, A or B?
16 Why did we spend so much time with this game? Because we wanted to show that almost all classification problems have a geometric interpretation, check out the next 3 slides 16
17 Left Bar Problem 1 Examples of class A Examples of class B Right Bar Here is the rule again. If the left bar is smaller than the right bar, it is an A, otherwise it is a B
18 Left Bar Problem 2 Examples of class A Examples of class B Right Bar Let me look it up here it is.. the rule is, if the two bars are equal sizes, it is an A. Otherwise it is a B
19 Left Bar Problem 3 Examples of class A Examples of class B Right Bar The rule again: if the square of the sum of the two bars is less than or equal to 100, it is an A. Otherwise it is a B. 19
20 Grasshoppers Antenna Length Katydids Abdomen Length 20
21 Antenna Length previously unseen instance = ??????? Abdomen Length We can project the previously unseen instance into the same space as the database. We have now abstracted away the details of our particular problem. It will be much easier to talk about points in space. Katydids Grasshoppers 21
22 Simple Linear Classifier If previously unseen instance above the line then class is Katydid else class is Grasshopper Katydids Grasshoppers R.A. Fisher
23 Classification Accuracy Predicted class Class = Katydid (1) Class = Grasshopper (0) Actual Class Class = Katydid (1) f 11 f 10 Class = Grasshopper (0) f 01 f 00 Number of correct predictions f 11 + f 00 Accuracy = = Total number of predictions f 11 + f 10 + f 01 + f 00 Number of wrong predictions f 10 + f 01 Error rate = = Total number of predictions f 11 + f 10 + f 01 + f 00 23
24 Confusion Matrix In a binary decision problem, a classifier labels examples as either positive or negative. Classifiers produce confusion/ contingency matrix, which shows four entities: TP (true positive), TN (true negative), FP (false positive), FN (false negative) Confusion Matrix Predicted positive (Y) Predicted negative (N) Positive (+) TP FN Negative (-) FP TN 24
25 The simple linear classifier is defined for higher dimensional spaces 25
26 we can visualize it as being an n-dimensional hyperplane 26
27 It is interesting to think about what would happen in this example if we did not have the 3 rd dimension 27
28 We can no longer get perfect accuracy with the simple linear classifier We could try to solve this problem by user a simple quadratic classifier or a simple cubic classifier.. However, as we will later see, this is probably a bad idea 28
29 Which of the Problems can be solved by the Simple Linear Classifier? 1) Perfect 2) Useless 3) Pretty Good Problems that can be solved by a linear classifier are call linearly separable
30 A Famous Problem R. A. Fisher s Iris Dataset. Virginica 3 classes 50 of each class The task is to classify Iris plants into one of 3 varieties using the Petal Length and Petal Width. Setosa Versicolor Iris Setosa Iris Versicolor Iris Virginica 30
31 We can generalize the piecewise linear classifier to N classes, by fitting N-1 lines. In this case we first learned the line to (perfectly) discriminate between Setosa and Virginica/Versicolor, then we learned to approximately discriminate between Virginica and Versicolor. Virginica Setosa Versicolor If petal width > (0.325 * petal length) then class = Virginica Elseif petal width 31
32 We have now seen one classification algorithm, and we are about to see more. How should we compare them? Predictive accuracy Speed and scalability time to construct the model time to use the model efficiency in disk-resident databases Robustness handling noise, missing values and irrelevant features, streaming data Interpretability: understanding and insight provided by the model 32
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