Element 2: Discuss basic computational intelligence methods

Feature Extraction Methods1. Feature Extraction Introduction2. T-test 3. Signal Noise Ratio4. Linear correlation coefficient5. Principle Component Analysis (PCA)6. Linear Discriminant Analysis (LDA)

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).

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

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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• …

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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 (n1 samples) Class 2 (n2 samples)

t-Test• Where and are the mean values for this variable

for the samples from class 1 and class 2 respectively

• And s1 and s2 are the corresponding sample variance.

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)

s1 and s2 the variance for each group

n1 and n2 the number of subjects in each group.

10987654321

T-test

t-test Example

Class 1 Class 225 516 1221 918 1332 19

| 22.4 11.6 |

40.3 26.85 5

2.948

t

2

1

2 2

( 22.4)

5 1

(25 22.4) (32 22.4)...

4 4

40.3

xs

On Class Practice

Try to calculate t value using ExcelWe have 49 X1 against 47 X2

Data File:Moodle – ISCG8042 – Topic 2 - TtestSample.xlsx (worksheet2 ‘Try do this’)

Example: worksheet1 ‘example’

On Class Practice con.

What’s your result?

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.

Signal Noise Ratio

Iris SNR by NecCom Breast-w SNR by NecCom

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.

Linear correlation coefficient (LCC)

On Class Practice

Try to calculate SNR and LCC

Data File:Moodle – ISCG8042 – Topic 2 - SNR_Sample.xlsxLCC_Sample.xlsx

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