Extracting Actionable Knowledge from Decision Trees

Yang, Qiang; Yin, Yin; Ling,; Jian, Pan

doi:10.1109/tkde.2007.250584

Cited by 92 publications

(90 citation statements)

References 19 publications

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“…In [6], novel post processing technique was used to extract actionable knowledge from decision tree. Customer relationship management was used as a case.…”

Section: Related Workmentioning

confidence: 99%

“…Knowledge Discovery in Databases (KDD) [3,6,13,14] is an active area of research that resolves the complexity mentioned above. Knowledge discovery in databases is the effort to understand, analyze, and eventually make use of the huge volume of data available.…”

Section: B Discrete Classesmentioning

confidence: 99%

“…5 and Naïve Bays to summarize data for selecting a group of customer. Then a novel algorithm [4][5][6][7] is used to extract actionable knowledge. This algorithm processes decision tree and obtain actions that are associated with attribute-value changes of the clients from one status (not a depositor) to another (depositor).…”

Section: Splitting According To Singlementioning

confidence: 99%

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Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Karim¹,

Rahman²

2013

JSEA

102

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Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naïve Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.

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“…In [6], novel post processing technique was used to extract actionable knowledge from decision tree. Customer relationship management was used as a case.…”

Section: Related Workmentioning

confidence: 99%

Section: B Discrete Classesmentioning

confidence: 99%

Section: Splitting According To Singlementioning

confidence: 99%

See 1 more Smart Citation

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Karim¹,

Rahman²

2013

JSEA

102

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“…Although there have been some reports of actionable knowledge discovery [8,9,10,16] and selecting actionable patterns/rules or interestingness measures in association rule mining [1,2,4,15,17], none of the previous research considers how to select actionable positive and negative sequential patterns. Very few papers study NSP mining [12,19,20,21,24,26,27], and most primarily focus on how to design a mining algorithm and how to improve the algorithm's efficiency.…”

Section: K/2] M=1mentioning

confidence: 99%

Select actionable positive or negative sequential patterns

Xiang

Liu

et al. 2015

IFS

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Abstract. Negative sequential patterns (NSP) refer to sequences with non-occurring and occurring items, and can play an irreplaceable role in understanding and addressing many business applications. However, some problems occur after mining NSP, the most urgent one of which is how to select the actionable positive or negative sequential patterns. This is due to the following factors: 1) positive sequential patterns (PSP) mined before considering NSP may mislead decisions; and 2) it is much more difficult to select actionable patterns after mining NSP, as the number of NSPs is much greater than PSPs. In this paper, an improved method of pruning uninteresting itemsets to fit for a selecting actionable sequential pattern (ASP) is proposed. Then, a novel and efficient method, called SAP, is proposed to select the actionable positive and negative sequential patterns. Experimental results indicate that SAP is very efficient in the selection of ASP. To the best of our knowledge, SAP is the best method for the selection of actionable positive and negative sequential patterns.

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“…1,2,5,[12][13][14] Decision tree algorithms, such as C4.5, 15 SBP 16,17 and others have also been used by many researchers to extract knowledge from databases that can be used by managers to make decisions. 18 In the case study in this paper, we make use of decision tree techniques for assisting a new pet insurance company to characterise its target market.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Implementation study: Using decision tree induction to discover profitable locations to sell pet insurance for a startup company

D'Souza¹,

Krasnodebski²,

Abrahams

2007

J Database Mark Cust Strategy Manag

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We demonstrate the use of decision tree induction, employing both C4.5 and Profi t Optimal (SBP) algorithms, to discover profi table locations for a young startup fi rm to sell their product, pet insurance. We use publicly available data including US Census data and veterinary surgery location data as our data sources and use the potential profi ts generated by each of the algorithms as key performance metrics. We show how our fi ndings link to general business behaviour and performance, by describing the implications of our fi ndings for marketing strategy at the pet insurance company.

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Extracting Actionable Knowledge from Decision Trees

Cited by 92 publications

References 19 publications

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Select actionable positive or negative sequential patterns

Implementation study: Using decision tree induction to discover profitable locations to sell pet insurance for a startup company

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Extracting Actionable Knowledge from Decision Trees

Cited by 92 publications

References 19 publications

Decision Tree and Na&#239;ve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Decision Tree and Na&#239;ve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Select actionable positive or negative sequential patterns

Implementation study: Using decision tree induction to discover profitable locations to sell pet insurance for a startup company

Contact Info

Product

Resources

About

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing