Variable Consistency Dominance-based Rough Set Approach Research Articles

Explaining and predicting customer churn by monotonic rules induced from ordinal data

In the course of a computational experiment on bank customer churn data, we demonstrate the explanatory and predictive capacity of monotonic decision rules. The data exhibit a partially ordinal character, as certain attribute value sets describing the clients are ordered and demonstrate a monotonic relationship with churn or non-churn outcomes. The data are structured by the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) prior to the induction of monotonic decision rules. The supervised learning is conducted using an extended version of VC-DRSA, implemented in RuLeStudio and RuleVisualization programs. The first one is designed to experiment with parameterized rule models, and the second one is used for visualization and a thorough examination of the rule model. The monotonic decision rules give insight into the bank data, characterizing loyal customers and the ones who left the bank. Such an approach is in line with explainable AI, aiming to obtain a transparent decision model, that can be easily understood by decision-makers. We also compare the predictive performance of monotonic rules with some well-known machine learning models.

Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid

To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Although the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various technical indicators simultaneously and the down-side risk of an investment decision is still underexplored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference-enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., buy, neutral, or sell) are preprocessed in two groups: straight-forward signals and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for those complicated signals (i.e., for signals that require imprecise judgments with perceived feeling of domain experts to identify patterns), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80 % classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.

A new approach and insightful financial diagnoses for the IT industry based on a hybrid MADM model

Financial performance is vital for information technology (IT) companies to survive intense global competition. Because of the complexity in the business environment and the rapidly advancing technologies, companies lack specific guidance to understand the implicit relationship among crucial financial indicators for improving prospects in a contextual approach. To resolve the aforementioned concern, this study proposed a new approach by combining the variable consistency dominance-based rough set approach (VC-DRSA) with the decision-making trial and evaluation laboratory (DEMATEL) technique to explore the complex relationship among financial variables and improve future performances. In addition, a fuzzy inference system was devised on the basis of the findings of the VC-DRSA and DEMATEL technique to examine granulized knowledge and implications. A group of real IT companies listed on the Taiwan stock market were used as an empirical case to present the benefits of the new approach. The results generated a set of decision rules that can be used for forecasting future performance prospects and diagnosing the directional influences of crucial variables to gain insights; certain strong decision rules were further examined using fuzzy inference to verify the obtained implications. The findings contribute to the financial applications of decision-making science and computational intelligence in practice.

Studies on ICT Usage in the Academic CampusUsing Educational Data Mining

Inthe era of competition, change and complexity, innovation in teaching and learning practices in higher education sector has become unavoidable criteria.One of the biggest challenges that higher education system faces today is to assessthe services provided through Information and Communication Technology(ICT) facilities installed in the campus. This paper studies the responses collected through survey on ICT, in the campusof the University of Burdwan, among the students and research scholarswith the help ofan effective data mining methodology Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) model to extract meaningful knowledge to improve the quality of managerial decisions in this sphere. It is an extended version of Dominance Rough Set Approach (DRSA) and is applied here to generate a set of recommendations that can help the university to improvise the existing services and augmenting the boundaries of ICT in future development.

Variable consistency dominance-based rough set approach to preference learning in multicriteria ranking

We present a methodology for non-statistical preference learning in multicriteria ranking based on Variable Consistency Dominance-based Rough Set Approach (VC-DRSA). A finite set of objects to be ranked is evaluated by a set of criteria, which are real-valued functions with ordinal or cardinal scales. Given the statement of a multicriteria ranking problem, the only objective information one can get is the dominance relation over the set of objects. The dominance relation is, however, too poor because it leaves many objects incomparable. To enrich this relation, and make the objects more comparable, a decision maker (DM) must supply some preference information revealing her/his value system with respect to multicriteria evaluations. We are considering a frequent case, when the preference information has the form of pairwise comparisons of some objects relatively well known to the DM, called reference objects. This information is thus composed of some decision examples on the reference objects. It is the input data for a method that learns the preferences of the DM. Since this information is prone to inconsistencies, we propose to structure it using VC-DRSA. Then, the pairs of objects that are sufficiently consistent serve as a basis for induction of a preference model. This model has the form of a set of “if …, then …” decision rules. Application of these rules on the whole set of objects to be ranked yields a fuzzy preference structure (directed weighted graph). This preference structure is then exploited using a ranking method, so as to work out a final recommendation. We propose a list of properties that helps to choose a proper ranking method. The methodology is illustrated by an example.

Water quality analysis using a variable consistency dominance-based rough set approach

Analysis and evaluation of water quality and its dynamics are of prime importance for water resources and environmental monitoring. Diverse methods such as multivariate statistics, time series analysis, and neural networks have been used for modeling and analysis of water quality indicators. Although these methods are useful to explore the main body of knowledge related to the water pollution problem, they are less effective for considering inherent uncertainties and vagueness in water pollution data. In this study, a variable consistency dominance-based rough set approach (VC-DRSA) was used to explore the underlying knowledge related to data for total dissolved solids (TDSs) in the Latyan Watershed, north of Tehran, Iran. Environmental parameters for the period of 2002–2007, including precipitation, river water temperature, runoff measured at 22 monitoring sites, and two products of the MODIS sensor (16-day NDVI and land surface temperature) were the explanatory variables. VC-DRSA was used in data mining analysis to explore the most effective and reliable rules for relating TDS data to the explanatory variables. Rule validation results show that the extracted rules were very effective and straightforward for examining the important relationships between the environmental parameters and TDS data. Application of the moving average filter in the TDS data led to decreased noise and a considerable reduction in the width of the boundary region between the lower and upper approximations.

Variable Consistency Dominance-based Rough Set Approach to formulate airline service strategies

This study differs from previous ones applying multivariate statistical analysis and multiple criteria decision-making (MCDM) methods. We use the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) to formulate airline service strategies by generating airline service decision rules that model passenger preferences for airline service quality. Flow graphs are applied to infer decision rules and variables. This combined method considers decision-maker inconsistency. The use of flow graphs to visualize rules makes them more reasonable and understandable than traditional methods. To validate the effectiveness of our model, a large sample is surveyed. Managerial improvements needed for carriers to achieve the aspired-to level of customer satisfaction are also discussed.

Variable-precision dominance-based rough set approach and attribute reduction

In this paper, a variable-precision dominance-based rough set approach (VP-DRSA) is proposed together with several VP-DRSA-based approaches to attribute reduction. The properties of VP-DRSA are shown in comparison to previous dominance-based rough set approaches. An advantage of VP-DRSA over variable-consistency dominance-based rough set approach in decision rule induction is emphasized. Some relations among the VP-DRSA-based attribute reduction approaches are investigated.

A novel decision rules approach for customer relationship management of the airline market

Customer churn means the loss of existing customers to a competitor. Accurately predicting customer behavior may help firms to minimize this loss by proactively building a lasting relationship with their customers. In this paper, the application of the factor analysis and the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) in the customer relationship management (CRM) of the airline market is introduced. A set of “if … then … ” decision rules are used as the preference model to classify customers by a set of criteria and regular attributes. The proposed method can determine the competitive position of an airline by understanding the behavior of its customers based on their perception of choice, and so develop the appropriate marketing strategies. A large sample of customers from an international airline is used to derive a set of rules and to evaluate its prediction ability.

Multi-criteria classification – A new scheme for application of dominance-based decision rules

We are considering the problem of multi-criteria classification. In this problem, a set of “ if … then …” decision rules is used as a preference model to classify objects evaluated by a set of criteria and regular attributes. Given a sample of classification examples, called learning data set, the rules are induced from dominance-based rough approximations of preference-ordered decision classes, according to the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA). The main question to be answered in this paper is how to classify an object using decision rules in situation where it is covered by (i) no rule, (ii) exactly one rule, (iii) several rules. The proposed classification scheme can be applied to both, learning data set (to restore the classification known from examples) and testing data set (to predict classification of new objects). A hypothetical example from the area of telecommunications is used for illustration of the proposed classification method and for a comparison with some previous proposals.