Dominance-based Rough Set Approach Research Articles

Incremental method of updating approximations in DRSA under variations of multiple objects

Dominance-based rough sets approach (DRSA) uses dominance relations to substitute equivalence relations in conventional rough set models so that it can handle preference-ordered information. Up to date, DRSA has been widely used in multi-criteria decision-making problems. In these real-life problems, however, since the collected data are evolving from time to time, there are often some variations of the attribute sets or object sets. In the dynamic information systems, the frequent update of the lower and upper approximations of DRSA is an necessary step for further updating attribute reducts and decision rules which are important for knowledge discovery and decision-making. Incrementally updating approximations is a type of effective methods to reduce the computational load when any variation occurs. Most of current studies on incremental methods only consider conventional rough set models and the situation when a single object varies in an information system. In this paper, we focus on the variations of object sets and discuss incremental methods of updating approximations of DRSA when multiple objects changed. The updating principles in different dynamic situations are given with detail proofs and the corresponding incremental algorithms are also developed. The experimental evaluations on 12 UCI data sets show that our proposed incremental approaches effectively reduce the computational time in comparison with the non-incremental approach as well as a typical incremental method in the literature.

Rough set theory based prognostic classification models for hospice referral.

BackgroundThis paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect.MethodsWe utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm.ResultsThe RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5.ConclusionsThis paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods—logistic regression, support vector machines and random forests—were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.

Dominance-based rough set approach for group decisions

The objective of this paper is to propose an approach to support group multicriteria classification. The approach is composed of three phases. The first phase exploits the knowledge provided by each decision maker to individually approximate the decision classes using rough approximation. The second phase seeks to combine the outputs of individual approximation phase into a collective decision table by using an appropriate aggregation procedure. The third phase uses the collective decision table in order to infer a set of collective decision rules, which synthesize the judgements and perspectives of the different decision makers and to permit the classification of all decision objects. The proposed approach relies on the Dominance-based Rough Set Approach (DRSA), which is used at two different levels. First, the DRSA is used during the first phase to approximate the input data relative to each decision maker. Second, the DRSA is used during the third phase to approximate the collective decision table and generate the collective decision rules. This paper presents the theoretical foundation of the proposed approach, three case studies using real-world data and a comparative study of recent similar proposals.

Selection of decision rules based on attribute ranking

Rule classifiers enhance understanding of data and enable to represent learned knowledge in an explicit structural form. To further improve this understanding and generalisation properties of predictors, the process of selection of decision rules can be executed. The paper presents a post-processing approach to this task. In a proposed research framework there were exploited rankings of attributes the rules refer to, thus transferring the established importance of features to the rules based on them. The attributes were weighted by selected statistical measures and machine learning algorithms. The rule classifiers were constructed in Dominance-Based Rough Set Approach and employed in the domain of stylometry for the task of authorship attribution.

COMBINING DRSA DECISION-RULES WITH FCA-BASED DANP EVALUATION FOR FINANCIAL PERFORMANCE IMPROVEMENTS

This study proposes a combined method to integrate soft computing techniques and multiple criteria decision making (MCDM) methods to guide semiconductor companies to improve financial performance (FP) – based on logical reasoning. The complex and imprecise patterns of FP changes are explored by dominance-based rough set approach (DRSA) to find decision rules associated with FP changes. Companies may identify its underperformed criterion (gap) to conduct formal concept analysis (FCA) – by implication rules – to explore the source criteria regarding the underperformed gap. The source criteria are analysed by decision making trial and evaluation laboratory (DEMATEL) technique to explore the cause-effect relationship among the source criteria for guiding improvements; in the next, DEMATEL-based analytical network process (DANP) can provide the influential weights to form an evaluation model, to select or rank improvement plans. To illustrate the proposed method, the financial data of a real semiconductor company is used as an example to show the involved processes: from performance gaps identification to the selection of five assumed improvement plans. Moreover, the obtained implication rules can integrate with DEMATEL analysis to explore directional influences among the critical criteria, which may provide rich insights and managerial implications in practice.

Multiple criteria ranking and choice with all compatible minimal cover sets of decision rules

We introduce a new multiple criteria ranking/choice method that applies Dominance-based Rough Set Approach (DRSA) and represents the Decision Maker’s (DM’s) preferences with decision rules. The DM provides a set of pairwise comparisons indicating whether an outranking (weak preference) relation should hold for some pairs of reference alternatives. This preference information is structured using the lower and upper approximations of outranking (S) and non-outranking (Sc) relations. Then, all minimal-cover (MC) sets of decision rules being compatible with this preference information are induced. Each of these sets is supported by some positive examples (pairs of reference alternatives from the lower approximation of a preference relation) and it does not cover any negative example (pair of alternatives from the upper approximation of an opposite preference relation). The recommendations obtained by all MC sets of rules are analyzed to describe pairwise outranking and non-outranking relations, using probabilistic indices (estimates of probabilities that one alternative outranks or does not outrank the other). Furthermore, given the preference relations obtained in result of application of each MC set of rules on a considered set of alternatives, we exploit them using some scoring procedures. From this, we derive the distribution of ranks attained by the alternatives. We also extend the basic approach in several ways. The practical usefulness of the method is demonstrated on a problem of ranking Polish cities according to their innovativeness.

Robustness analysis for decision under uncertainty with rule-based preference model

We consider decision under uncertainty as a multi-attribute classification problem where a set of acts is described by outcomes gained with given probabilities. The Decision Maker (DM) provides desired classification for a small subset of reference acts. Such preference information is structured using Dominance-based Rough Set Approach (DRSA), and the resulting lower approximations of the quality class unions are used as an input for construction of an aggregate preference model. We induce all minimal-cover sets of rules being compatible with the non-ambiguous assignment examples, and satisfying some additional requirements that may be imposed by the DM. Applying such compatible instances of the preference model on a set of all acts, we draw conclusions about the certainty of recommendation assured by different minimal-cover sets of rules. In particular, we analyze the diversity of class assignments, assignment-based preference relations, and class cardinalities. Then, we solve an optimization problem to get a univocal (precise) classification for all acts, taking into account the robustness concern. This optimization problem admits incorporation of additional indirect and imprecise preferences in form of desired class cardinalities and assignment-based pairwise comparisons. Finally, we extend the proposed approach to group decision under uncertainty. We present a set of indicators and outcomes giving an insight into the spaces of consensus and disagreement between the DMs.

An approach to rule extraction for product service system configuration that considers customer perception

At present, manufacturers tend to bundle a product with its related services as a product service system, to meet customer needs and achieve competitive advantage. Configuring a product service system involves selecting and combining appropriate product and service components, to satisfy individual customer needs. One crucial step to rapidly configure a product service system is to develop formalised configuration rules, which show the relationships between product service components and customer needs, including those expressed as perception needs. In this study, a rough set-based approach is proposed to acquire configuration rules. First, an information table is built by combining the results of factor analysis and questionnaire survey. Then, customer segmentation is accomplished by clustering. Based on the information table and customer segmentation, a dominance-based rough set approach is used to extract the configuration rules. Finally, a weighbridge is selected for the case study to validate the proposed approach.

Combined soft computing model for value stock selection based on fundamental analysis

The stock selection problem is one of the major issues in the investment industry, which is mainly solved by analyzing financial ratios. However, considering the complexity and imprecise patterns of the stock market, obvious and easy-to-understand investment rules, based on fundamental analysis, are difficult to obtain. Therefore, in this paper, we propose a combined soft computing model for tackling the value stock selection problem, which includes dominance-based rough set approach, formal concept analysis, and decision-making trial and evaluation laboratory technique. The objectives of the proposed approach are to (1) obtain easy-to-understand decision rules, (2) identify the core attributes that may distinguish value stocks, (3) explore the cause–effect relationships among the attributes or criteria in the strong decision rules to gain more insights. To examine and illustrate the proposed model, this study used a group of IT stocks in Taiwan as an empirical case. The findings contribute to the in-depth understanding of the value stock selection problem in practice.

Dominance-based rough set approach: An application case study for setting speed limits for vehicles in speed controlled zones

Speed management represents an important strategy in order to improve road safety, because a strong relationship is between speed and crash occurrence and severity. Speed limits enforcement is the main measure to control operating speeds but to obtain the compliance of drivers, the limits must be safe but also credible.This means that road users have to regard the speed limit as logical under given conditions and so a speed limit is credible if it fits the image evoked by the road.This paper describes the development of a Decision Support System (DSS) for the selection of safe and credible speed limits for speed zones. The proposed DSS is based on Dominance-based Rough Set Approach (DRSA), which presents interesting advantages in terms of transparency and manageability with respect to many other decision support competitive methodologies. In fact DRSA, after getting the preferred information necessary to set up the decision model, in terms of exemplary decisions, allows to build a multi-criteria model expressed in terms of “if …, then …” decision rules.The proposed multi-criteria decision approach aims to suggest to decision-makers a safe and credible speed limit for speed zones, taking into consideration many factors such as accident rate, roadway geometry, roadway development, traffic and others.

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.

Ant-based extraction of rules in simple decision systems over ontological graphs

Abstract In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems

Parallel computing of approximations in dominance-based rough sets approach

Computation of approximation is a critical step for applying rough sets methodologies in knowledge discovery and data mining. As an extension of classic rough sets theory, Dominance-based Rough Sets Approach (DRSA) can process information with preference-ordered attribute domain and then can be applied in multi-criteria decision analysis and other related works. Efficiently computing approximations is helpful for reducing the time of making decisions based on DRSA. Parallel computing is an effective way to speed up the process of computation. In this paper, several strategies of decomposition and composition of granules in DRSA are proposed for computing approximations in parallel and the corresponding parallel algorithm is designed. A numerical example is employed to validate the feasibility of these strategies. The experimental evaluations on a multi-core environment showed that the parallel algorithm can obviously reduce the time of computing approximations in DRSA.

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.

Rough approximation of a preference relation by multi-decision dominance for a multi-agent conflict analysis problem

Multi-attribute group decision-making (MAGDM) has evoked increasing attention in recent years. Meanwhile, many valuable approaches have been developed to solve various MAGDM problems. In this paper, we consider a MAGDM problem in the presence of multi-attribute and multi-decision decision making with preference, namely the MA&MD decision problem. It involves the assignment of objects (actions), evaluated based on a set of conditional attributes, to pre-defined and preference-ordered multi-decision making. The actions are described by a finite set of conditional attributes and decision attributes. Both types of attribute take the values from their domain with preference order. In order to construct a comprehensive preference evaluation model that could be used to support the optimal choice task, we define two dominance relations, one on the condition attribute set and the other on the decision attribute set. We then present the lower and upper approximations of a preference relation defined by the decision attribute set based on a multi- decision preference dominance relation. Meanwhile, we propose an approach to decision making based on the rough set model established in this paper. The approach to decision making is derived from the lower approximation of decision classes with a preference dominance relation. The idea and decision rule are applied to solving a multi-agent conflict analysis decision problem. This method addresses limitations of the Pawlak conflict analysis model and thus improves on that model. Furthermore, to give practical significance to this management decision making approach, we present two extended models of the multi-decision preference dominance-based rough set as well as the corresponding decision making method. Moreover, we compare the proposed approach to previous studies of dominance-based rough set approaches to multiple attribute (criteria) decision making. The main contribution of this paper is twofold. One is to establish a generalization of the classical dominance-based rough set approach, i.e., the model of multi-decision preference dominance-based rough set. Another is to present a new approach to deal with the multi-agent conflict analysis decision making problem based on the proposed multi-decision rough set approach.

Prediction of antifungal activity of gemini imidazolium compounds.

The progress of antimicrobial therapy contributes to the development of strains of fungi resistant to antimicrobial drugs. Since cationic surfactants have been described as good antifungals, we present a SAR study of a novel homologous series of 140 bis-quaternary imidazolium chlorides and analyze them with respect to their biological activity against Candida albicans as one of the major opportunistic pathogens causing a wide spectrum of diseases in human beings. We characterize a set of features of these compounds, concerning their structure, molecular descriptors, and surface active properties. SAR study was conducted with the help of the Dominance-Based Rough Set Approach (DRSA), which involves identification of relevant features and relevant combinations of features being in strong relationship with a high antifungal activity of the compounds. The SAR study shows, moreover, that the antifungal activity is dependent on the type of substituents and their position at the chloride moiety, as well as on the surface active properties of the compounds. We also show that molecular descriptors MlogP, HOMO-LUMO gap, total structure connectivity index, and Wiener index may be useful in prediction of antifungal activity of new chemical compounds.

Innovative Mining Equipment: Key Factors for Successful Implementation

With increasing social pressures over the past two decades, mining companies have become more concerned with occupational health and safety (OHS). This change in mentality has had an impact on the choice of new equipment introduced underground. Introduction of innovative mining equipment must now meet the double challenge of improving both productivity and OHS. However, innovative equipment does not by itself ensure success. The aim of this study is to identify the key factors that underlie successful introduction of new equipment in underground mines. We used a multi-attribute decision-aid tool, namely the dominance-based rough-set approach. Ten innovative equipment projects carried out in an underground mine in Quebec were analysed. The tool allowed simultaneous identification of the most relevant factors and critical thresholds thereof to establish levels of performance based on four indicators, namely cost per meter drilled, cost per hour of use, availability ratio and accident rate. Two factors were found most relevant to all four indicators of performance: the skill requirement and acceptance of the equipment by the operators. Seat quality was identified as a contributor to improving accident rate, while standardisation of the new equipment relative to the old contributed to productivity. Taking into consideration these key factors and their critical threshold values will help the directors of the participating mine to meet with success in terms of both productivity and OHS in future introduction of innovative equipment projects.

Using a Dominance-based Rough Set Approach for Analysing Business Indicators

Abstract The study demonstrates how to extract essential information from a set of financial and non-financial business indicators, leading to a better understanding of company performance. To this end, use Rough Set Theory and the Dominance principle are used. The usage is justified by the possibility of there being uncertain data - uncertainty that needs to be treated analytically to yield an essential set of effectively consistent information. The analysis was based on the Brazilian publication Exame Melhores e Maiores 2013, which lists the 500 largest companies in various economic sectors ordered by net sales. The methods employed here can be extended to take other financial and non-financial indicators into account to infer patterns often hidden in the data. Furthermore, this study revealed the importance of broadening the analysis of enterprise indicators when the goal is, for example, to produce a more critical view for investment decisions and to measure the performance of probable competitors.

Comparative analysis of targeted metabolomics: Dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

BackgroundMetabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. ResultsPrincipal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. ConclusionDRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition–decision relationships not otherwise revealed in metabolomics data.

Incremental update of approximations in dominance-based rough sets approach under the variation of attribute values

Dominance-based Rough Sets Approach (DRSA) has received much attention since it is able to acquire knowledge from information with preference ordered attribute domains and decision classes. In many real-life applications, the information systems may evolve over time dynamically. In a dynamic information system, the obtained knowledge, e.g., approximations in DRSA, need to be updated for decision making and other related tasks. As a useful technique, the incremental update can be applied to process dynamic information with revising the obtained knowledge. In this paper, we propose an incremental approach for maintaining approximations of DRSA when attribute values vary over time. Some numerical examples illustrate that the incremental approach can renew approximations of DRSA without beginning from scratch. Experimental evaluations show that the incremental algorithm can effectively reduce the computational time in comparison with the non-incremental one when the ratio of the attribute values varied is less than a threshold.