Recent Progress in Learning Decision Lists by Prepending Inferred Rules
نویسنده
چکیده
This paper describes a new algorithm for learning decision lists that operates by prepending successive rules to the front of the list under construction. By contrast, the classic algorithm operates by appending successive rules to the end of the decision list under construction. The new algorithm is demonstrated in the majority of cases to produce smaller classifiers that provide improved predictive accuracy in less time than the classic algorithm.
منابع مشابه
Learning Decision Lists by Prepending Inferred Rules
This paper describes a new algorithm for learning decision lists that operates by prepending successive rules to front of the list under construction. This contrasts with the original decision list induction algorithm which operates by appending successive rules to end of the list under construction.. The new algorithm is demonstrated in the majority of cases to produce smaller classifiers that...
متن کاملAlternative Strategies for Decision List Construction
This work surveys well-known approaches to building decision lists. Some novel variations to strategies based on default rules for the most common class and insertion of new rules before the default rule are presented. These are expected to offer speed up in the construction of the decision list as well as compression of the length of the list. These strategies and a testing regime have been im...
متن کاملRule prepending and post-pruning approach to incremental learning of decision lists
A decision list [1], DL, is de"ned as a list of ordered pairs (1 ,< ), (1 ,< ),2 , (1 ,< ) . These pairs are called nodes and they are denoted as N ,N ,2,N , whereN "(1 ,< ).N is called default node of D ̧. Each 1 is a test whose outcome is either True or False, each < is a class label, and 1 is the constant function, True. D ̧ de"nes a classi"cation function as follows: for any input x, DL(x) is...
متن کاملFalling Rule Lists
Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highes...
متن کاملAn optimization approach to learning falling rule lists
A falling rule list is a probabilistic decision list for binary classification, consisting of a series of if-then rules with antecedents in the if clauses and probabilities of the desired outcome (“1”) in the then clauses. Just as in a regular decision list, the order of rules in a falling rule list is important – each example is classified by the first rule whose antecedent it satisfies. Unlik...
متن کامل