1. Element 2: Discuss basic computational intelligence methods

2. Feature Extraction Methods 1.Feature Extraction Introduction 2.T-test 3.Signal Noise Ratio 4.Linear correlation coefficient 5.Principle Component Analysis (PCA) 6.Linear Discriminant Analysis (LDA)

3. Feature Extraction: Definition  When the input data to a system is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information)  The input data will be transformed into a reduced representation set of data.  The transforming is called feature extraction. The obtained reduced set of data is called feature, (also named features vector).

4. Feature Extraction: Motivation Data compression: Efficient storage Data characterization –Data understanding: analysis Discovering data characteristics –Clustering: unknown labels –Classification: known labels –Pre-processing for further analysis Tracking Visualization: reduction of visual clutter Comparison/classification Search: large collections of data sets Database management: efficient retrieval 4

5. Feature Extraction Applications Activity recognition Place tracking Face recognition Remote sensing Bioinformatics Structural engineering Robotics Biometrics GIS (Geographic information system) Semiconductor defect analysis Earthquake engineering Plant biology Medicine Sensing … 5

6. t-Test t-Test is a calculated ranking number for each variable to define how well this variable discriminates two classes. Given two class samples on selected variable Compute t as Class 1 (n 1 samples) Class 2 (n 2 samples)

7. t-Test Where and are the mean values for this variable for the samples from class 1 and class 2 respectively And s 1 and s 2 are the corresponding sample variance.

8. 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Spelling Test Scores A t-test allows us to compare the means of two groups and determine how likely the difference between the two means occurred by chance. The calculations for a t-test requires three pieces of information: - the difference between the means (mean difference) s 1 and s 2 the variance for each group n 1 and n 2 the number of subjects in each group. 10 9 8 7 6 5 4 3 2 1 T-test

9. t-test Example Class 1Class 2 255 1612 219 1813 3219

10. On Class Practice Try to calculate t value using Excel We have 49 X 1 against 47 X 2 Data File: Moodle – ISCG8042 – Topic 2 - TtestSample.xlsx (worksheet2 ‘Try do this’) Example: worksheet1 ‘example’

11. On Class Practice con. What’s your result?

12. Signal Noise Ratio SNR is a calculated ranking number for each variable to define how well this variable discriminates two classes. The following formula is used: where:  1 and  2 are the corresponding standard deviations.

13. Signal Noise Ratio Iris SNR by NecComBreast-w SNR by NecCom

14. Linear correlation coefficient (LCC) LCC is a measurement of the strength of a linear relationship between a dependent variable (i.e. the output class, y) and an independent variable (i.e. feature, x) The correlation value varies from –1 to 1. A value of 0 suggests no linear correlation, while values nearer to –1 or 1 mean negatively or positively correlated variables.

15. Linear correlation coefficient (LCC)

16. On Class Practice Try to calculate SNR and LCC Data File: Moodle – ISCG8042 – Topic 2 - SNR_Sample.xlsx LCC_Sample.xlsx

17. On Class Practice Data – Iris.txt (Neucom format) and your own data (if applicable) Method: PCA, LDA, SNR Software – Neucom v0.919 – Steps: Visualization->PCA – Steps: Visualization->LDA – Steps: Data Analysis->SNR