Rule Discovery Technique Using Genetic Programming Combined with Apriori Algorithm

Niimi, Ayahiko; Tazaki, Eiichiro

doi:10.1007/3-540-44418-1_28

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…In the future, we will research the following four topics: apply the method to other veri® cations (Niimi & Tazaki, 2000a, 2000b, 2000c; discuss the conversion algorithm from the association rule to a decision tree with high accuracy; extend the proposed method to multivalue classi® cation problems; study a theoretical analysis about the mechanism of the over® tting.…”

Section: Discussionmentioning

confidence: 98%

Combined method of genetic programming and association rule algorithm

Niimi¹,

Tazaki²

2001

Applied Artificial Intelligence

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Section: Discussionmentioning

confidence: 98%

Combined method of genetic programming and association rule algorithm

Niimi¹,

Tazaki²

2001

Applied Artificial Intelligence

Self Cite

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“…Every decision tree classifies a single class. Once the evolutionary process has finished the best individual of the evolution is converted into classification rules, as [24], [4], [5] have proposed. The corresponding authors claim that their approaches are able to generate comprehensible rules.…”

Section: ) Previous Workmentioning

confidence: 99%

“…these calculate the fitness of the individual by measuring the result of the classification and the "simplicity" of the solutions, as an instance [3], [4], [5], [17]. Other works just divide the resulting decision tree in rules, for example [24]. In those cases the GP is favoring the shortest solutions as T a , T b , T c instead of a bigger tree that hold more rules.…”

Section: Different Conditionmentioning

confidence: 99%

Evolving decision rules to predict investment opportunities

García-Almanza

Tsang

2008

Int. J. Autom. Comput.

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Abstract-This work is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce the prediction of these cases is difficult.In a previous work, we have introduced Evolving Decision Rules (EDR) to detect financial opportunities. The objective of EDR is to classify the minority class (positive cases) in imbalanced environments. EDR provides a range of classifications in order to find the best balance between not making mistakes and not missing opportunities.The goals of this paper are: 1) to show that EDR produces a range of solutions to suit the investor's preferences and 2) to analyze the factors that benefit the performance of EDR. A series of experiments was performed, EDR was tested using a data set from the London Financial Market. In order to analyse the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different level of complexity. Finally, an illustrative example was provided in order to show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets.Experimental results show that: 1) EDR offers a range of solutions to fit the risk guidelines of different types of investors and 2) a bigger collection of rules is able to classify more positive cases in imbalanced environments.

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“…A hybrid model consisting of the Apriori algorithm and a constraint-based genetic algorithm (ACBGA) was designed for efficient classification of electronic ballast data [2]. In another approach, association rules generated by an Apriori miner are further refined by the aid of a genetic programming module [3,4]. Using support vector machines (SVM), a constructive method for extraction of association rules is proposed in which the classification knowledge is encoded in to an SVM classification tree (SVMT) and linguistic association rules are then decoded by using the trained SVMT [5].…”

Section: Introductionmentioning

confidence: 99%

Concept Classification Using a Hybrid Data Mining Model

Brown

Forouraghi

2009

2009 21st IEEE International Conference on Tools With Artificial Intelligence

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Apriori is a well-known algorithm which is used extensively in market-basket analysis and data mining. The algorithm is used for learning association rules from transactional data bases and is based on simple counting procedures. In this paper we propose enhancements to Apriori which allow it to perform concept classification similar to the way decision tree algorithms learn. Specifically, training examples are modified and treated as transactional data and the results are verified and further improved by C4.5 decision tree and k-means clustering algorithms, respectively. To demonstrate the novelty of the enhanced Apriori algorithm, we present a hybrid data mining model (HDMM) which identifies atrisk students based on their academic performance and other pertinent data.

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Rule Discovery Technique Using Genetic Programming Combined with Apriori Algorithm

Cited by 5 publications

References 2 publications

Combined method of genetic programming and association rule algorithm

Combined method of genetic programming and association rule algorithm

Evolving decision rules to predict investment opportunities

Concept Classification Using a Hybrid Data Mining Model

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