Question

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Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, case-based reasoning).

                                                                                                    

Answer 1

EAGER CLASSIFICATION VS LAZY CLASSIFICATION:

In the field of artificial intelligence both classifications have their own importance in problem solving.

• In eager classification learning methods generally construct the general , explicit description of the target function.Where as the lazy classification simply store the data , generalizing beyond the data is postponed until an explicit request is made.
• Eager classification generally deals with the construction of the explicit description of the target function.
• Eager classification is faster than the lazy classification because it constructs the generalization model before receiving the new tuples for classification.

ADVANTAGES OF THE LAZY CLASSIFICATION:

• It is incremental means problem solving ability increases with each newly presented case.
• Suitable for the incomplete and complex type of problems.
• Ease of maintenance. ( lazy learner adapts automatically to the changes)

DISADVANTAGES OF THE LAZY CLASSIFICATION:

• If a problem is large this becomes time consuming (LONGER TESTING TIME).
• The possibility of error as in CBR the problem domain is highly dynamic.
• The problem of overly noisy data.

ADVANTAGES OF THE EAGER CLASSIFICATION:

• It is a faster approach for classification of data as it takes less time for prediction.
• The results are highly accurate . As it is totally based on decision making to find out solutions.

DISADVANTAGES OF THE EAGER CLASSIFICATION:

• The training of dataset is performed under high supervision (high skills are required).
• The solution needs to be chosen accurately to support the better decision making.
• The whole solution commits on the single hypothesis

I hope this works for you .