1. Computational Biology
Classification
(some parts taken from
Introduction to Data Mining by
Tan, Steinbach, Kumar)
Lecture Slides Week 10

2. MBG404 Overview
Processing
Pipelining
Generation
Data
Storage
Mining

3. Data Mining Data mining Machine learning noun Digital Technology.
The process of collecting, searching through, 
and analyzing a large amount of data in a database, 
as to discover patterns or relationships: 
e.g.: the use of data mining to detect fraud.
Machine learning
a branch of artificial intelligence, concerning the construction and study of systems that can learn from data. For example, a machine learning system could be trained on messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new messages into spam and non-spam folders.

4. Application in Biology
Data exploration
Microarrays
Next generation sequencing
Prediction
microRNAs
Protein secondary structure

5. Parametrization

6. Types of Attributes There are different types of attributes Nominal
Examples: ID numbers, eye color, zip codes
Ordinal
Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short}
Interval
Examples: calendar dates, temperatures in Celsius or Fahrenheit.
Ratio
Examples: temperature in Kelvin, length, time, counts

7. Data Type Distinctness (nominal) Order (ordinal) Addition (interval)
Equal or unequal
Order (ordinal)
>,<,>=,<=
Addition (interval)
+,-
Multiplication (ratio)
*,/

8. Data Quality Missing data Noise False measurements Outliers
Duplicate data
Precision
Bias
Accuracy

9. Data Preprocessing Aggregation Sampling Dimensionality Reduction
Feature subset selection
Feature creation
Discretization and Binarization
Attribute Transformation

10. Aggregation Variation of Precipitation in Australia
Standard Deviation of Average Monthly Precipitation
Standard Deviation of Average Yearly Precipitation

11. Dimensionality Reduction: PCA

12. Similarity/Dissimilarity for Simple Attributes
p and q are the attribute values for two data objects.

13. Similarity Eucledian distance Simple matching coefficient Correlation
Jaccard coefficient
Correlation
Cosine similarity
...

14. Sampling Curse of dimensionality Feature selection
Dimensionality reduction
Principal component analysis
Aggregation
Mapping of data to different space

15. Sampling Dividing samples into Using not all samples from both sets
Training set
Test set
Using not all samples from both sets

16. Classification Examples with known classes (labels)
Learn rules of how the attributes define the classes
Classify unknown samples into the appropriate class

17. Classification Workflow

18. End Theory I
5 min Mindmapping
10 min Break

19. Practice I

20. Exploring Data (Irises)
Download the file Iris.txt
Follow along

21. Exploring Data
Frequencies
Percentiles
Mean, Median
Visualizations

22. Data Selection
Selecting columns
Filtering rows

23. Data Transformation
Discretize
Continuize
Feature construction

24. Visualizations

25. End Practice I
Break 15 min

26. Theory II

27. Classification Workflow

28. Illustrating Classification Task

29. Example of a Decision Tree
categorical
continuous
class
Splitting Attributes
Refund
Yes No NO
MarSt
Single, Divorced
Married
TaxInc NO
< 80K
> 80K NO
YES
Training Data
Model: Decision Tree

30. General Structure of Hunt’s Algorithm
Let Dt be the set of training records that reach a node t
General Procedure:
If Dt contains records that belong the same class yt, then t is a leaf node labeled as yt
If Dt is an empty set, then t is a leaf node labeled by the default class, yd
If Dt contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset. Dt ?

31. Hunt’s Algorithm Refund Refund Refund Marital Marital Status Status
Don’t
Cheat
Yes No
Don’t
Cheat
Refund
Don’t
Cheat
Yes No
Marital
Status
Single,
Divorced
Married
Refund
Don’t
Cheat
Yes No
Marital
Status
Single,
Divorced
Married
Taxable
Income
< 80K
>= 80K

32. How to Find the Best Split
Before Splitting: M0 A? B?
Yes No
Yes No
Node N1
Node N2
Node N3
Node N4 M1 M2 M3 M4
M12
M34
Gain = M0 – M12 vs M0 – M34

33. Underfitting and Overfitting
Underfitting: when model is too simple, both training and test errors are large

34. Overfitting due to Noise
Decision boundary is distorted by noise point

35. Overfitting due to Insufficient Examples
Lack of data points in the lower half of the diagram makes it difficult to predict correctly the class labels of that region
- Insufficient number of training records in the region causes the decision tree to predict the test examples using other training records that are irrelevant to the classification task

36. Cost Matrix C(i|j): Cost of misclassifying class j example as class i
PREDICTED CLASS
ACTUAL CLASS
C(i|j)
Class=Yes
Class=No
C(Yes|Yes)
C(No|Yes)
C(Yes|No)
C(No|No)
C(i|j): Cost of misclassifying class j example as class i

37. Cost-Sensitive Measures
Precision is biased towards C(Yes|Yes) & C(Yes|No)
Recall is biased towards C(Yes|Yes) & C(No|Yes)
F-measure is biased towards all except C(No|No)

38. Receiver Operating Characteristic (ROC) Curve
(TP,FP):
(0,0): declare everything to be negative class
(1,1): declare everything to be positive class
(1,0): ideal
Diagonal line:
Random guessing
Below diagonal line:
prediction is opposite of the true class

39. Using ROC for Model Comparison
No model consistently outperform the other
M1 is better for small FPR
M2 is better for large FPR
Area Under the ROC curve
Ideal:
Area = 1
Random guess:
Area = 0.5

40. End Theory II
5 min Mindmapping
10 min Break

41. Practice II

42. Learning
Supervised (Classification)
Classification
Decision tree
SVM

43. Classification Use the iris.txt file for classification
Follow along as we classify

44. Classification Use the orangeexample file for classification
We are interested if we can distinguish between miRNAs and random sequences with the selected features
Try yourself