Lecture1.ppt
- 2. Classification • Introduction • Statistical Based Algorithm • Distance Based Algorithm • Tree Based Algorithm • Rule Based Algorithm • Neural Network Based Algorithm • Combining Technique
- 3. Introduction • Classification involves mapping of input data to appropriate classes. • Def: Given a database D = {t1 , t2 , ... , tn } of tuples (items, records) and a set of classes C = { C 1, ... , Cm }, the classification problem is to define a mapping f: D C where each ti is assigned to one class. A class, Cj , contains precisely those tuples mapped to it; that is, Cj = {ti |f(ti ) = Cj , 1 ≤ i ≤ n and ti E D}. • The problem is implemented in two phases: 1.Create a specific model by evaluating the training data. 2. Apply the model to classifying tuples from the target database.
- 4. Introduction
- 5. Introduction • Issues In Classification:. 1. Missing Data 2. Measuring Performance.
- 6. Missing Data There are many approaches to handle the missing data: • Ignore the missing data. • Assume a value for the missing data. • Assume a special value for the missing data.
- 7. Measuring Performance and Accuracy • Classification accuracy is usually calculated by determining the percentage of tuples placed in the correct class. • Given a specific class and a database tuple may or may not be assigned to that class while its actual membership may or may not be in that class. This gives us four quadrants: • True positive (TP): 𝑡𝑖 predicted to be in 𝐶𝑗 and is actually in it. • False positive (FP): 𝑡𝑖 predicted to be in 𝐶𝑗 but is not actually in it. • True negative (TN): 𝑡𝑖 not predicted to be in 𝐶𝑗 and is not actually in it. • False negative (FN): 𝑡𝑖 not predicted to be in 𝐶𝑗 but is actually in it.