A15 Probabilistic Classification

Linear discriminant analysis (LDA) is one of the techniques used pattern recognition to classify two or more classes of objects and in this case the separation of the 2 kinds of chips using the 4 features aextracte from previous activities.

The classification works by taking the combinations of features based on differences. Say the objects’ features are linearly separable, we can use linear discriminant model (LDA) formula given as:

SOURCE: http://people.revoledu.com/kardi/tutorial/LDA/LDA.html#LDA

This is a summary of the data used in activity 14:

Table 1.

Procedure:
1. LDA
After processing the data in Table 1 as input x in the implemented LDA formula in Scilab, each sample was easily classified as either belonging in class 1 or 2 depending on its value f.
2. Determine if f1>f2:
By comparison, a large value of f1 would mean that this object belonged to class 1 and and class 2 if f1 is small.
The LDA worked successfully in assigning the 24 samples to its respective class.

ASSESSMENT: 10/10 because I successfully implemented LDA in scilab and used EXCEL to summarize the data into tables :)