Add to ORKGThis 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 provide improved predictive accuracy than those produced by the original decision list induction algorithm. Area: machine learning Subarea: learning decision list
We learn decision lists over a space of features that are constructed from the data. A practical mac...
Induction methods have recently been found to be useful in a wide variety of business related proble...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...
This paper describes a new algorithm for learning decision lists that operates by prepending success...
Abstract. We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists. Alt...
This work surveys well-known approaches to building decision lists. Some novel variations to strateg...
We introduce a new learning algorithm for decision lists to allow features that are constructed from...
A decision list [1], DL, is defined as a list of ordered pairs $\{(T_1,V_1), (T_2,V_2),... , (T_r,V_...
. We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms....
We show an algorithm that learns decision lists via equivalence queries, provided that a set G inclu...
A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as ...
In this paper, we propose a novel method to learn deci-sion lists (classifiers as sets of rules) wit...
Abstract. In most approaches to ensemble methods, base classifiers are decision trees or decision st...
We present a learning algorithm for decision lists which allows features that are constructed from t...
A decision structure is a simple and powerful tool for organizing decision processes. It differs fro...
We learn decision lists over a space of features that are constructed from the data. A practical mac...
Induction methods have recently been found to be useful in a wide variety of business related proble...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...
This paper describes a new algorithm for learning decision lists that operates by prepending success...
Abstract. We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists. Alt...
This work surveys well-known approaches to building decision lists. Some novel variations to strateg...
We introduce a new learning algorithm for decision lists to allow features that are constructed from...
A decision list [1], DL, is defined as a list of ordered pairs $\{(T_1,V_1), (T_2,V_2),... , (T_r,V_...
. We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms....
We show an algorithm that learns decision lists via equivalence queries, provided that a set G inclu...
A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as ...
In this paper, we propose a novel method to learn deci-sion lists (classifiers as sets of rules) wit...
Abstract. In most approaches to ensemble methods, base classifiers are decision trees or decision st...
We present a learning algorithm for decision lists which allows features that are constructed from t...
A decision structure is a simple and powerful tool for organizing decision processes. It differs fro...
We learn decision lists over a space of features that are constructed from the data. A practical mac...
Induction methods have recently been found to be useful in a wide variety of business related proble...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...