A fuzzy method for discovering cost-effective actions from data

Kalanat, Nasrin; Shamsinejadbabaki, Pirooz; Saraee, Mohamad

doi:10.3233/ifs-141357

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…The sharp behavior of the methods results in unreal estimation about the profit of actions. In [14], [15] Kalanat et al utilized Fuzzy Decision Tree (FDT) to overcome this problem.…”

Section: Related Workmentioning

confidence: 99%

Extracting Actionable Knowledge From Social Networks Using Structural Features

2020

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In conventional data mining methods, the output is either a description of input data or a prediction of unseen data. But the real-world problems usually require interventions in order to alter the current data specifications towards a desirable goal. Actionable knowledge discovery is a field of study specifically developed for this matter. Existing methods rarely tackled the problem of extracting actionable knowledge from social networks. Moreover, due to the dependencies among the underlying network data, extracted actions should be evaluated since the changes suggested by the actions may not be described by the model constructed so far. This enforces the refinement of the model to preserve the quality of extracted actions. In this paper we propose a new method for action mining which incorporates an action evaluation process overcoming the mentioned problem while focusing specifically on social network data. Such data contains valuable information based on the links inside the network where a change in some feature values may result in a chain of changes in others due to the dependencies conveyed by the links in the network. We use a state-of-the-art structural feature extraction method to capture the information of the dependencies inside the network. Our proposed method iteratively updates structural features which are incorporated in the action extraction process. In this process, we thoroughly examine the effects of the application of actions by discovering the impact of possible changes in the network. We call this phenomenon ''change propagation''. According to our experimentations, our method outperforms the state-of-the-art methods in terms of action effectiveness and reliability with comparable efficiency. INDEX TERMS Social networks mining, action mining, actionable knowledge discovery, structural features, change propagation, change-awareness.

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“…The sharp behavior of the methods results in unreal estimation about the profit of actions. In [14], [15] Kalanat et al utilized Fuzzy Decision Tree (FDT) to overcome this problem.…”

Section: Related Workmentioning

confidence: 99%

Extracting Actionable Knowledge From Social Networks Using Structural Features

2020

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“…Instead of class probabilities, Nasrin Kalanat et al introduced [10,11] fuzzy based methods which makes use of fuzzy membership to mine the profitable actions from the data. Their methods build the fuzzy decision tree and postprocess the tree to mine the actions to improvise the fuzzy profit by taking fuzzy costs into account.…”

Section: Literature Survey and Related Workmentioning

confidence: 99%

“…Fixed size bit patterns vector for the path from the root to leaf L3 is formed as: Attribute values to reach L3 shall be Gender=NULL, Service Level=medium, Call Charges=low. Thus, bit patterns vector of the path of L3 is (11,010,10). Similarly, bit pattern vectors of the other potential Candidates L3 and L5 are also formed.…”

Section: Bit Patterns Matrix Representation Of the Petmentioning

confidence: 99%

“…Till now, very less study has been done on extracting profit maximizing knowledge automatically from the machine learning models. Though some of the past research has addressed this problem, in that they treated the problem as a 2-class problem and presented the methods to convert the customer from class C2 (attritor) to class C1 (non-attritor) only [9][10][11][12][13]. Moreover, there is no specific focus on computational performance while achieving the objective of profit maximization.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

Muneiah

Subbarao

2019

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Enterprises often classify their customers based on the degree of profitability in decreasing order like C1, C2, ..., Cn. Generally, customers representing class Cn are zero profitable since they migrate to the competitor. They are called as attritors (or churners) and are the prime reason for the huge losses of the enterprises. Nevertheless, customers of other intermediary classes are reluctant and offer an insignificant amount of profits in different degrees and lead to uncertainty. Various data mining models like decision trees, etc., which are built using the customers’ profiles, are limited to classifying the customers as attritors or non-attritors only and not providing profitable actionable knowledge. In this paper, we present an efficient algorithm for the automatic extraction of profit-maximizing knowledge for business applications with multi-class customers by postprocessing the probability estimation decision tree (PET). When the PET predicts a customer as belonging to any of the lesser profitable classes, then, our algorithm suggests the cost-sensitive actions to change her/him to a maximum possible higher profitable status. In the proposed novel approach, the PET is represented in the compressed form as a Bit patterns matrix and the postprocessing task is performed on the bit patterns by applying the bitwise AND operations. The computational performance of the proposed method is strong due to the employment of effective data structures. Substantial experiments conducted on UCI datasets, real Mobile phone service data and other benchmark datasets demonstrate that the proposed method remarkably outperforms the state-of-the-art methods.

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“…Generality is referred to as on how many objects an action can be applied, based on this definition there are two categories of action mining methods. The first category covers those actions which can be applied on multiple groups of objects called action rules [9]- [11] and the second category finds separate action for each object [12]- [14]. Among these, the second category discovers the best actions.…”

Section: ) Actionable Rulesmentioning

confidence: 99%

Actionable Knowledge Discovery for Increasing Enterprise Profit, Using Domain Driven-Data Mining

Batra

Rehman

2019

IEEE Access

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Actionable Knowledge Discovery approaches to extract the business and technical significant actions/patterns to support direct decision making. These actions suggest how to transform an object from an undesirable status to a desirable status by incurring less cost and high profit. This article aims to propose a work that generates actionable patterns efficiently. It reduces the search space and number of iterations for attribute value change during action generation. Performance of the proposed method is compared with Yang's method and OF-CEAMA on the basis of four parameters i.e. the total number of rules required for action generation, run time of the methods, the total number of generated actions, total net profit and time and space complexity. Experiments have been carried out on four datasets retrieved from UCI Machine learning repository. Experimental results show that the proposed work takes less time than the other two methods to extract actions for all datasets. Also, the number of rules required to generate actions are less than the other two methods. Results also suggest that a decrease in execution time does not compromise the information and proposed work generates the same actions and net profit. Moreover, the proposed work tries to transfer an object from undesired status to the desired status. INDEX TERMS Actions, actionable knowledge, cost matrix, CRM, domain knowledge, domain driven, Knowledge Discovery, net profit.

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A fuzzy method for discovering cost-effective actions from data

Cited by 12 publications

References 20 publications

Extracting Actionable Knowledge From Social Networks Using Structural Features

Extracting Actionable Knowledge From Social Networks Using Structural Features

An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

Actionable Knowledge Discovery for Increasing Enterprise Profit, Using Domain Driven-Data Mining

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