The Find-S algorithm is used to find the most specific hypothesis that is consistent with all positive instances in the training dataset. It:
- Starts with the most specific hypothesis.
- Considers only positive instances.
- Ignores all negative instances.
Given Training Dataset:
| CGPA | Interactiveness | Practical Knowledge | Communication Skills | Logical Thinking | Interest | Job Offer |
|---|---|---|---|---|---|---|
| ≥9 | Yes | Excellent | Good | Fast | Yes | Yes |
| ≥9 | Yes | Good | Good | Fast | Yes | Yes |
| ≥8 | No | Good | Good | Fast | No | No |
| ≥9 | Yes | Good | Good | Slow | No | Yes |
- The hypothesis covers all positive instances
- It ignores negative instances
- Thus, the Find-S algorithm successfully finds the most specific hypothesis consistent with positive data
Limitations of Find-S Algorithm
- Ignores Negative Instances – It only uses positive examples, missing useful information from negative ones.
- Finds Only One Hypothesis – It gives only one hypothesis, though multiple consistent ones may exist.
- Fails with Noisy Data – It cannot handle errors or inconsistencies in the training data.
Hence, Candidate Elimination Algorithm is proposed to overcome these limitations by considering both positive and negative instances.