lazy learners and other classication methods

lazy learners and other classication methods

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  M.Rajshree M.SC(IT) Nadar saraswathi collegeof arts&science
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  Lazy learners  lazylearning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries.  Lazy learners do less work while training data is given and more work when classification of a test tuple is given.
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   The classificationmethods discussed so far in this chapter—decision tree induction, Bayesian classification, rule-based classification, classification by backpropagation, support vector machines, and classification based on association rule mining—are all examples of eager learners  A lazy learner simply stores the training data and only when it sees a test tuple starts generalization to classify the tuple based on its similarity to the stored training tuple
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   Building amodel from a given set of training data  Applying the model to a given set of testing data  Eager Learners like Bayesian Classification, Rule-based classification, support vector machines, etc. will construct a classification model before receiving new tuple when a set of training tuple is given
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  k-Nearest-Neighbor Classifiers  The k-nearest-neighbormethod was first described in the early 1950s.  Nearest-neighbor classifiers are based on learning by analogy, that is, by comparing a given test tuple with training tuples that are similar to it.  The training tuples are described by n attributes. Each tuple represents a point in an n-dimensional space.
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   In thisway, all of the training tuples are stored in an n-dimensional pattern space. When given an unknown tuple, a k-nearest- neighbor classifier searches the pattern space for the k training tuples that are closest to the unknown tuple  distance between two points or tuples, say, X1 = (x11, x12…. x1n) and X2 = (x21, x22…x2n)  When given a test tuple, a k-nearest neighbor classifier searches the pattern space for the k training tuples that are closest to the test tuple.  These k training tuples are the k “nearest neighbors” of the test tuple
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  Case-Based Reasoning  Base-basedreasoning is the process of solving new problems based on the solutions of similar past problems.  These classifiers use a database of problem solutions to solve new problems.  The case-based reasoner tries to combine the solutions of the neighboring training cases in order to propose a solution for the new case
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   Case-based reasoning(CBR) classifiers use a database of problem solutions to solve new problems.  Unlike nearest-neighbor classifiers, which store training tuples as points in Euclidean space, CBR stores the tuples or cases‖ for problem solving as complex symbolic descriptions.  Business applications of CBR include problem resolution for customer service help desks, where cases describe product-related diagnostic problems.
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   CBR hasalso been applied to areas such as engineering and law, where cases are either technical designs or legal rulings, respectively.  Medical education is another area for CBR, where patient case histories and treatments are used to help diagnose and treat new patients.  The case-based reasoner tries to combine the solutions of the neighboring training cases in order to propose a solution for the new case.  The case-based reasoner may employ background knowledge and problem-solving strategies in order to propose a feasible combined solution.
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  Other classification methods Data mining involves six common classes of tasks. Anomaly detection, Association rule learning, Clustering, Classification, Regression, Summarization. Classification is a major technique in data mining and widely used in various fields.  Classification is a technique where we categorize data into a given number of classes
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   Binary Classification:Classification task with two possible outcomes Eg: Gender classification (Male / Female)  Multi class classification: Classification with more than two classes. In multi class classification each sample is assigned to one and only one target label Eg: An animal can be cat or dog but not both at the same time  Multi label classification: Classification task where each sample is mapped to a set of target labels (more than one class). Eg: A news article can be about sports, a person, and location at the same time.
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  Naïve Bayes  NaiveBayes algorithm based on Bayes’ theorem with the assumption of independence between every pair of features. Naive Bayes classifiers work well in many real-world situations such as document classification and spam filtering.  This algorithm requires a small amount of training data to estimate the necessary parameters. Naive Bayes classifiers are extremely fast compared to more sophisticated methods.
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  Fuzzy Set Approaches Fuzzy Set Theory is also called Possibility Theory. This theory was proposed by Lotfi Zadeh in 1965 as an alternative the two-value logic and probability theory  This theory allows us to work at a high level of abstraction. It also provides us the means for dealing with imprecise measurement of data.  fuzzy set approach an important consideration is the treatment of data from a linguistic view point from this has developed an approach that uses linguistically quantified propositions to summarize the content of a data base by providing a general characterization of the analyzed data