Activity 15: Probabilistic Classification

This activity is an extension of the previous activity, Pattern Recognition. However instead of using minimum distance classification to determine class membership what we used is Linear Discriminant Analysis or LDA. As defined in [1], Linear Discriminant Analysis finds a linear transformation of two predictors that yields a new set of transformed values that provide a more accurate discrimination than either predictor alone.

To fully understand the math of LDA, I recommend to visit the site in [2]. So applying LDA as described in [2] to the data gathered in the previous activity resulted to the following.

The table above shows the classification obtained for each test object. Each table contains 5 test objects belonging to a specific class. For example the first table above shows the result in classifying 5 test objects which actually belong to the class of long leaves. Same follows for the second table - rectangular leaf, third table - flower, fourth table - 25 centavo coin.

Class membership is determined by the largeness in value of the calculated discriminant function. As can be seen from the table above, all the test objects were correctly classified resulting to 100% accuracy of the method used.

For this activity, I will give myself a grade of 10 for I was able to do all the required task.

I thank Jica Monsanto for the useful discussions.


References:

[1] http://www.dtreg.com/lda.htm
[2] http://people.revoledu.com/kardi/tutorial/LDA/LDA.html#LDA