Multi-level organization and summarization of the discovered rules

This paper addresses the well-known classification task of data mining, where the objective is to predict the class which an example belongs to. Discovered knowledge is expressed in the form of high-level, easy-to-interpret classification rules. In order to discover classification rules, we propose a hybrid decision tree/genetic algorithm method. The central idea of this hybrid method involves the concept of small disjuncts in data mining, as follows. In essence, a set of classification rules can be regarded as a logical disjunction of rules, so that each rule can be regarded as a disjunct. A small disjunct is a rule covering a small number of examples. Due to their nature, small disjuncts are error prone. However, although each small disjunct covers just a few examples, the set of all small disjuncts can cover a large number of examples, so that it is important to develop new approaches to cope with the problem of small disjuncts. In our hybrid approach, we have developed two genetic algorithms (GA) specifically designed for discovering rules covering examples belonging to small disjuncts, whereas a conventional decision tree algorithm is used to produce rules covering examples belonging to large disjuncts. We present results evaluating the performance of the hybrid method in 22 real-world data sets. _____________________________________________________________________________________________

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“…Liu et al present a new technique for organizing discovered rules in different levels of detail [18]. The algorithm consists of two steps.…”

Section: Related Workmentioning

confidence: 99%

A hybrid decision tree/genetic algorithm method for data mining

Carvalho

Freitas

2004

Information Sciences

142

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“…Liu, Hu, and Hsu (2000a) propose the use of a hierarchical representation consisting of general rules and exceptions in order to replace the usual flat representation model where too many association rules hamper the understanding of the underlying data. The same approach is followed in Liu, Hu, and Hsu (2000b) in order to obtain a good summary of the knowledge contained in an arbitrary set of rules. In some way, ART takes a further step in that direction, as we will see in the following sections.…”

Section: Related Workmentioning

confidence: 99%

ART: A Hybrid Classification Model

et al. 2004

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Abstract. This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

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“…Liu et al present a new technique for organizing discovered rules in different levels of detail [12]. The algorithm consists of two steps.…”

Section: Related Workmentioning

confidence: 99%

A genetic-algorithm for discovering small-disjunct rules in data mining

Carvalho

Freitas

2002

Applied Soft Computing

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This paper addresses the well-known classification task of data mining, where the goal is to discover rules predicting the class of examples (records of a given data set). In the context of data mining, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. At first glance, this is not a serious problem, since the impact on predictive accuracy should be small. However, although each small disjunct covers few examples, the set of all small disjuncts can cover a large number of examples. This paper presents evidence that this is the case in several data sets. This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic algorithm approach. In essence, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm (C4.5), while examples belonging to small disjuncts are classified by a genetic algorithm specifically designed for discovering small-disjunct rules. We present results comparing the predictive accuracy of this hybrid system with the prediction accuracy of three versions of C4.5 alone in eight public domain data sets. Overall, the results show that our hybrid system achieves better predictive accuracy than all three versions of C4.5 alone.

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Multi-level organization and summarization of the discovered rules

Cited by 70 publications

References 6 publications

A hybrid decision tree/genetic algorithm method for data mining

A hybrid decision tree/genetic algorithm method for data mining

ART: A Hybrid Classification Model

A genetic-algorithm for discovering small-disjunct rules in data mining

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