Add to ORKGThis 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
Abstract. In most approaches to ensemble methods, base classifiers are decision trees or decision st...
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they ...
A decision structure is a simple and powerful tool for organizing decision processes. It differs fro...
This paper describes a new algorithm for learning decision lists that operates by prepending success...
This work surveys well-known approaches to building decision lists. Some novel variations to strateg...
A decision list [1], DL, is defined as a list of ordered pairs $\{(T_1,V_1), (T_2,V_2),... , (T_r,V_...
Abstract. We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists. Alt...
We introduce a new learning algorithm for decision lists to allow features that are constructed from...
Abstract. We evaluate the power of decision tables as a hypothesis space for supervised learning alg...
A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as ...
We show an algorithm that learns decision lists via equivalence queries, provided that a set G inclu...
Incremental learning algorithms are better suited for online learning tasks in principle. However, d...
In this paper, we propose a novel method to learn deci-sion lists (classifiers as sets of rules) wit...
Induction of decision rules plays an important role in machine learning. Themain advantage of decis...
AbstractAn incremental algorithm generating satisfactory decision rules and a rule post-processing t...
Abstract. In most approaches to ensemble methods, base classifiers are decision trees or decision st...
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they ...
A decision structure is a simple and powerful tool for organizing decision processes. It differs fro...
This paper describes a new algorithm for learning decision lists that operates by prepending success...
This work surveys well-known approaches to building decision lists. Some novel variations to strateg...
A decision list [1], DL, is defined as a list of ordered pairs $\{(T_1,V_1), (T_2,V_2),... , (T_r,V_...
Abstract. We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists. Alt...
We introduce a new learning algorithm for decision lists to allow features that are constructed from...
Abstract. We evaluate the power of decision tables as a hypothesis space for supervised learning alg...
A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as ...
We show an algorithm that learns decision lists via equivalence queries, provided that a set G inclu...
Incremental learning algorithms are better suited for online learning tasks in principle. However, d...
In this paper, we propose a novel method to learn deci-sion lists (classifiers as sets of rules) wit...
Induction of decision rules plays an important role in machine learning. Themain advantage of decis...
AbstractAn incremental algorithm generating satisfactory decision rules and a rule post-processing t...
Abstract. In most approaches to ensemble methods, base classifiers are decision trees or decision st...
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they ...
A decision structure is a simple and powerful tool for organizing decision processes. It differs fro...