To extract rules from a decision tree, one rule is created for each path from the root to a leaf node. Multiple Regression and Correlation Analysis, Classification and Prediction: Regression Analysis. "contentUrl": "https://slideplayer.com/slide/13513750/82/images/11/Rule-Based+Classification.jpg", Learn_One_Rule adopts a greedy depth-first strategy.

In this way, the rule fires when no other rule is satisfied. Because the path to each leaf in a decision tree corresponds to a rule, we can consider decision tree induction as learning a set of rules simultaneously. "width": "1024" "@context": "http://schema.org", The ordering may be class based or rule-based. This sequential learning of rules is in contrast to decision tree induction. ", ",

Because the rules are extracted directly from the tree, they are mutually exclusive and exhaustive. "@type": "ImageObject", }, 21

"description": "Multiple linear regression is an extension of straight-line regression so as to involve more than one predictor variable. ", Our current rule grows to become: The process repeats, where at each step, we continue to greedily grow rules until the resulting rule meets an acceptable quality level. Attributes regarding each applicant include their age, income, education level, residence, credit rating, and the term of the loan. }, 8 "width": "1024" Discriminant Analysis Discriminant analysis is a technique for analyzing data when the criterion or dependent variable is categorical and the predictor. During the next iteration, we again consider the possible attribute tests and end up selecting credit rating = excellent. Therefore, the order of the rules does not matterthey are unordered. Rule-Based ClassificationRule Induction Using a Sequential Covering Algorithm Rules are learned for one class at a time. Data Mining: A Closer Look Chapter Data Mining Strategies (p35) Moh! C4.5 orders the class rule sets so as to minimize the number of false-positive errors.

If more than one rule is triggered, then What if they each specify a different class We need a conflict resolution strategy to figure out which rule gets to fire and assign its class prediction to X. Most rule-based classification systems use a class-based rule-ordering strategy.

"description": "The rule ordering scheme prioritizes the rules beforehand. "@context": "http://schema.org", "name": "Rule-Based Classification", Each time a rule is learned, the tuples covered by the rule are removed, and the process repeats on the remaining tuples. }, 27 Using IF-THEN Rules for Classification. }, 4 The THEN-part is the rule consequent. We append it to the condition, so that the current rule becomes: IF income = high THEN loan_decision =accept. Non Linear Regression How can we model data that does not show a linear dependence? To extract rules from a decision tree, one rule is created for each path from the root to a leaf node. In general, the values of the predictor variables are known. The method of least squares shown above can be extended to solve for w0, w1, and w2. The rules need not necessarily be of high coverage. Each splitting criterion along a given path is logically ANDed to form the rule antecedent ( IF part). Typically, rules are grown in a general-to-specific manner. Rule Induction Using a Sequential Covering AlgorithmSuppose our training set, D, consists of loan application data.

{ If a rule is satisfied by X, the rule is said to be triggered. IF-THEN rules can be extracted directly from the training data using a sequential covering algorithm. Given a tuple, X, from a class labelled data set, D. let ncovers be the number of tuples covered by R, ncorrect be the number of tuples correctly classified by R and |D| be the number of tuples in D. We can define the coverage and accuracy of R as. C4.5 orders the class rule sets so as to minimize the number of false-positive errors. "description": "Rule Extraction from a Decision Tree. {

Rule-Based ClassificationRule Induction Using a Sequential Covering Algorithm The Learn_One_Rule procedure finds the best rule for the current class, given the current set of training tuples. We append it to the condition, so that the current rule becomes: IF income = high THEN loan_decision =accept }, 15

"contentUrl": "https://slideplayer.com/slide/13513750/82/images/4/Rule-Based+Classification.jpg", This sequential learning of rules is in contrast to decision tree induction. "width": "1024" ", { "description": "Which is easily solved by the method of least squares using software for regression analysis. independent or predictor variables and a dependent or response variable. "@type": "ImageObject", By exhaustive, there is one rule for each possible attribute-value combination, so that this set of rules does not require a default rule. How can we prune the rule set For a given rule antecedent, any condition that does not improve the estimated accuracy of the rule can be pruned, thereby generalizing the rule. Thank you! In the context of data mining, the predictor variables are the attributes of interest describing the tuple. Rule-Based Classification

With class-based ordering, the classes are sorted in order of decreasing importance, such as by decreasing order of prevalence. suppose Learn One Rule finds that the attribute test income = high best improves the accuracy of our current (empty) rule. { If more than one rule is triggered, then What if they each specify a different class? Rule Induction Using a Sequential Covering AlgorithmEach time we add an attribute test to a rule, the resulting rule should cover more of the accept tuples. Rule-Based ClassificationWith rule-based ordering, the rules are organized into one long priority list, according to some measure of rule quality such as accuracy, coverage, or size or based on advice from domain experts. An IF-THEN rule is an expression of the form. If more than one rule is triggered, then What if they each specify a different class? { Each time a rule is learned, the tuples covered by the rule are removed, and the process repeats on the remaining tuples. "contentUrl": "https://slideplayer.com/slide/13513750/82/images/8/Rule-Based+Classification.jpg", "description": "The rule\u2019s consequent contains a class prediction. Rule-Based Classification }, 17 With rule ordering, the triggering rule that appears earliest in the list has highest priority, and so it gets to fire its class prediction. ", "@context": "http://schema.org", "name": "Rule-Based Classification", Suppose our training set, D, consists of loan application data. "description": "Rule Induction Using a Sequential Covering Algorithm. "width": "1024" That is, the triggering rule with the most attribute tests is fired. In comparison with a decision tree, the IF-THEN rules may be easier for humans to understand, particularly if the decision tree is very large. "@type": "ImageObject", "name": "Rule-Based Classification", ",

", { The regression coefficients can be estimated using this method with the following equations: Multiple linear regression is an extension of straight-line regression so as to involve more than one predictor variable. Rule-Based ClassificationTriggering does not always mean firing since there may be more than one rule that is satisfied! "name": "Linear Regression", Each time it is faced with adding a new attribute test (conjunct) to the current rule, it picks the one that most improves the rule quality, based on the training samples. "@context": "http://schema.org", For conflict resolution, C4.5 adopts a class-based ordering scheme. "contentUrl": "https://slideplayer.com/slide/13513750/82/images/7/Rule-Based+Classification.jpg", "@context": "http://schema.org",

Non Linear Regression Consider a cubic polynomial relationship given by To convert this equation to linear form, we define new variables: Which is easily solved by the method of least squares using software for regression analysis.

(That is, X = (x1, x2, : : : , xn).). "@context": "http://schema.org",

"width": "1024" We append by adding the attribute test as a logical conjunct to the existing condition of the rule antecedent. ", }, Data Mining Classification: Alternative Techniques. Attributes regarding each applicant include their age, income, education level, residence, credit rating, and the term of the loan. { "width": "1024" "contentUrl": "https://slideplayer.com/slide/13513750/82/images/21/Rule+Induction+Using+a+Sequential+Covering+Algorithm.jpg", "name": "Rule-Based Classification", An example of a multiple linear regression model based on two predictor attributes or variables, A1 and A2, is y = w0+w1x1+w2x2 where x1 and x2 are the values of attributes A1 and A2, respectively, in X. IF condition THEN conclusion. Rule-Based ClassificationRule Induction Using a Sequential Covering Algorithm IF-THEN rules can be extracted directly from the training data using a sequential covering algorithm. Our training data set, D, contains data of the form (x1, y1), (x2, y2), , (x|D|, y|D|), where the Xi are the n-dimensional training tuples with associated class labels, yi. That is. If R1 is the only rule satisfied, then the rule fires by returning the class prediction for X. Rules are learned for one class at a time. "@context": "http://schema.org", For example, what if a given response variable and predictor variable have a relationship that may be modelled by a polynomial function? "@type": "ImageObject", "@type": "ImageObject", { { ",

We start off with an empty rule and then gradually keep appending attribute tests to it. "width": "1024" By applying transformations to the variables, we can convert the nonlinear model into a linear one that can then be solved by the method of least squares. It allows response variable y to be modelled as a linear function of, say, n predictor variables or attributes, A1, A2, , An, describing a tuple, X. "contentUrl": "https://slideplayer.com/slide/13513750/82/images/22/Prediction+What+if+we+would+like+to+predict+a+continuous+value%2C+rather+than+a+categorical+label.jpg", }, 24 Straight-line regression analysis involves a response variable, y, and a single predictor variable, x. ", ", We think you have liked this presentation.

"description": "Rule Extraction from a Decision Tree. Rule Generation from Decision Tree Decision tree classifiers are popular method of classification due to it is easy understanding However, decision tree. Linear Regression Straight-line regression analysis involves a response variable, y, and a single predictor variable, x. "contentUrl": "https://slideplayer.com/slide/13513750/82/images/12/Rule-Based+Classification.jpg", The process continues until the terminating condition is met, such as when there are no more training tuples or the quality of a rule returned is below a user-specified threshold. Most rule-based classification systems use a class-based rule-ordering strategy. { Applications The General Linear Model. R1: IF age = youth AND student = yes THEN buys computer = yes. 6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models. "@context": "http://schema.org", ", The condition in the default rule is empty. That is, all of the rules for the most prevalent (or most frequent) class come first, the rules for the next prevalent class come next, and so on. Hunts Algorithm CIT365: Data Mining & Data Warehousing Bajuna Salehe. Attributes regarding each applicant include their age, income, education level, residence, credit rating, and the term of the loan. The ordering may be class based or rule-based. "@context": "http://schema.org", Polynomial regression is a special case of multiple regression. "@type": "ImageObject", Although it is easy to extract rules from a decision tree, we may need to do some more work by pruning the resulting rule set. It groups all rules for a single class together, and then determines a ranking of these class rule sets. "name": "Rule-Based Classification", Each splitting criterion along a given path is logically ANDed to form the rule antecedent (IF part). We need a conflict resolution strategy to figure out which rule gets to fire and assign its class prediction to X. "@type": "ImageObject", { Rule-Based ClassificationRule Extraction from a Decision Tree Decision trees can become large and difficult to interpret.

It can be modelled by adding polynomial terms to the basic linear model.

Numeric prediction is the task of predicting continuous (or ordered) values for given input. Prediction What if we would like to predict a continuous value, rather than a categorical label? Numeric prediction is the task of predicting continuous (or ordered) values for given input. "name": "Rule-Based Classification", Each splitting criterion along a given path is logically ANDed to form the rule antecedent (IF part). "description": "Rule Extraction from a Decision Tree.

Rule-Based ClassificationWhat if there is no rule satisfied by X? "@context": "http://schema.org", By applying transformations to the variables, we can convert the nonlinear model into a linear one that can then be solved by the method of least squares. "@type": "ImageObject", The response variable is what we want to predict. It is the simplest form of regression, and models y as a linear function of x. The IF-part of a rule is known as the rule antecedent or precondition. The condition in the default rule is empty. "name": "Rule-Based Classification", coverage(R1) = 2\/ % accuracy (R1) = 2\/2 = 100%. "name": "Prediction What if we would like to predict a continuous value, rather than a categorical label",

}, 25 Rule Induction Using a Sequential Covering Algorithm. It is a statistical methodology that was developed by Sir Frances Galton (1822 1911), a mathematician who was also a cousin of Charles Darwin.