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Constructing Nominal XofN Attributes
 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence
, 1995
"... Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations. An XofN is a set containing one or more attributevalue pairs. For a g ..."
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Cited by 15 (6 self)
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Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations. An XofN is a set containing one or more attributevalue pairs. For a given instance, its value corresponds to the number of its attributevalue pairs that are true. The promising preliminary experimental results, on both artificial and realworld domains, show that constructing new nominal attributes in the form of XofN representations can significantly improve the performance of selective induction in terms of both higher prediction accuracy and lower theory complexity. 1 Introduction A wellknown elementary limitation of selective induction algorithms is that when tasksupplied attributes are not adequate for describing hypotheses, their performance in terms of prediction accuracy and/or theory complexity is poor. To overcome this limitation, constructiv...
Constructing New Attributes for Decision Tree Learning
, 1996
"... A wellknown fundamental limitation of selective induction algorithms is that when tasksupplied attributes are not adequate for, or directly relevant to, describing hypotheses, their performance in terms of prediction accuracy and/or theory complexity is poor. One solution to this problem is constru ..."
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Cited by 8 (3 self)
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A wellknown fundamental limitation of selective induction algorithms is that when tasksupplied attributes are not adequate for, or directly relevant to, describing hypotheses, their performance in terms of prediction accuracy and/or theory complexity is poor. One solution to this problem is constructive induction. It constructs, by using tasksupplied attributes, new attributes that are expected to be more appropriate than the tasksupplied attributes for describing the target concepts. This thesis focuses on constructive induction with decision trees as the theory description language. It explores: (1) novel approaches to constructing new binary attributes using existing constructive operators, and (2) novel methods of constructing new nominal and new continuousvalued attributes based on a newly proposed constructive operator. The thesis investigates a fixed rulebased approach to constructing new binary attributes for decision tree learning. It generates conjunctions from producti...
A Comparison of Constructive Induction with Different Types of New Attribute
, 1996
"... : This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation fun ..."
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Cited by 2 (1 self)
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: This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With different option settings, it can construct four different types of new attribute, but all other factors are fixed. The study reveals that conjunctive and disjunctive representations have very similar performance in terms of prediction accuracy and theory complexity on a variety of concepts, even on DNF and CNF concepts that are usually thought to be suited only to one of the two kinds of representation. In addition, the study demonstrates that the stronger representation power of MofN than conjunction and disjunction and the stronger representation power of XofN than these three types of new attribute can...
Constructing Nominal XofN Attributes
"... Most constructive induction researchers focus only on new boolean attributes This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations An XofN is a Bet containing one or more attributevalue pairs For a give ..."
Abstract
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Most constructive induction researchers focus only on new boolean attributes This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations An XofN is a Bet containing one or more attributevalue pairs For a given instance, its value corresponds to the number of its at tributevalue pairs that are true The promising preliminary experimental results, on both artificial and realworld domains, show that constructing new nominal attributes in the form of XofN representations can significantly improve the performance of selective induction in terms of both higher prediction accuracy and lower theory complexity 1
Effects of Different Types of New Attribute on Constructive Induction
, 1996
"... This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping crite ..."
Abstract
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This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With different option settings, it can construct four different types of new attribute, but all other factors are fixed. The study reveals that conjunctive and disjunctive representations have very similar performance in terms of prediction accuracy and theory complexity on a variety of concepts. Moreover, the study demonstrates that the stronger representation power of MofN than conjunction and disjunction and the stronger representation power of XofN than these three types of new attribute can be reflected in the performance of decision tree learning. 1. Introduction To improve the performance of selective induction systems, construc...