Other Classification Methods
March 30, 2023
Other Classification Methods
Data mining is the process of discovering and extracting hidden patterns from different types of data to help decision-makers make decisions. Associative classification is a common classification learning method in data mining, which applies association rule detection methods and classification to create classification models.
Bing Liu Et Al was the first to propose associative classification, in which he defined a model whose rule is “the right-hand side is constrained to be the attribute of the classification class”.An associative classifier is a supervised learning model that uses association rules to assign a target value.
Other Classification Methods
Genetic Algorithms :
Genetic Algorithm: based on an analogy to biological evolution
An initial population is created consisting of randomly generated rules
Each rule is represented by a string of bits
E.g., if A1 and ¬A2 then C2 can be encoded as 100 o If an attribute has k > 2
values, k bits can be used.
• Based on the notion of survival of the fittest, a new population is formed to
consist of the fittest rules and their offsprings . The fitness of a rule is
represented by its classification accuracy on a set of training examples.
• Offsprings are generated by crossover and mutation. The process continues until a
population P evolves when each rule in P satisfies a prespecified threshold. Slow
but easily parallelizable
Rough Set Approach:
• Rough sets are used to approximately or ―roughly‖ define equivalent classes.
• A rough set for a given class C is approximated by two sets: a lower approximation
(certain to be in C) and an upper approximation (cannot be described as not
belonging to C).
• Finding the minimal subsets (reducts) of attributes for feature reduction is
NP-hard
but a discernibility matrix (which stores the differences between attribute values
for each pair of data tuples) is used to reduce the computation intensity
Fuzzy Set approaches
• Fuzzy logic uses truth values between 0.0 and 1.0 to represent the degree of
membership (such as using fuzzy membership graph).
• Attribute values are converted to fuzzy values
e.g., income is mapped into the discrete categories {low, medium, high} with fuzzy
values calculated.
For a given new sample, more than one fuzzy value may apply.
• Each applicable rule contributes a vote for membership in the categories.
• Typically, the truth values for each predicted category are summed, and these sums
are combined.
Interview Questions :
1. Explain about K-Nearest Neighbor classifiers
2. Exaplain about Case- Based Reasoning
3. Explain about Genetic Algorithms
4. Explain about Rough Set Approach
5. Explain about Fuzzy set Approaches
6. Explain about Linear and Multiple Regression
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