JNTU B.Tech III MACHINE LEARNING Previous Question Paper (September - 2021 - R18)

The Candidate Elimination Algorithm is a machine learning method used to infer a concept from a set of positive and negative training examples. It maintains the Version Space, which represents all hypotheses consistent with the observed data, by simultaneously tracking the most specific hypotheses (S) and the most general hypotheses (G).

The algorithm proceeds as follows:

• Initialize S to the most specific hypothesis (all attributes null) and G to the most general hypothesis (all attributes wildcards).
• For each positive example, generalize S to include the example and remove any hypotheses in G that are inconsistent with it.
• For each negative example, specialize G to exclude the example and remove any hypotheses in S that are inconsistent with it.

Consider learning the concept of 'Enjoy Sport'. If the first example is (Sunny, Warm, Normal, Strong, Cool, Change) = Positive, S becomes (Sunny, Warm, Normal, Strong, Cool, Change). If the next example is (Rainy, Warm, Normal, Light, Warm, Same) = Negative, G is specialized by replacing wildcards with values that exclude this specific instance, such as (Sunny, ?, ?, ?, ?, ?) or (?, Warm, ?, ?, ?, ?). The process continues until S and G converge to a single hypothesis or the version space becomes empty.

The Version Space and Candidate Elimination algorithm provide a formal framework for understanding concept learning, but they have specific limitations and characteristics:

• Convergence: The algorithm converges to the true target concept only if the training data is noise-free and the target concept is contained within the chosen hypothesis space.
• Noise Sensitivity: It is highly sensitive to noisy data. A single mislabeled example can cause the version space to collapse (S and G become inconsistent), leading to an empty set.
• Computational Complexity: Maintaining the entire version space can be computationally expensive as the number of hypotheses grows exponentially with the number of attributes.
• Incomplete Data: If the training data is insufficient, the version space remains large, meaning the algorithm cannot uniquely identify the target concept, leaving multiple hypotheses as candidates.
• Incremental Learning: A key strength is its ability to process examples incrementally, updating the S and G boundaries without needing to re-examine previous data.