Dominance-based Rough Set Theory Research Articles

Fusing multiple interval-valued fuzzy monotonic decision trees

As a powerful knowledge mining technique for ordinal classification tasks, dominance-based rough set theory has many advantages but also some issues. Sensitivity to noisy information means that even a single mislabeled sample can lead to substantial fluctuations in the approximation calculations. Another issue is that most monotonic classifiers can only handle real-valued data and cannot directly deal with interval-valued data, which is common in practical applications. To address these issues, a tree-based fusion learning method for monotonic classification of interval-valued attributes, named FM-IFMDT, has been proposed. Its functional structure comprises three components: (i) The proposed robust β-precision interval-valued fuzzy dominance neighborhood rough set model (β-IFDNRS) can adaptively identify and filter noise samples. (ii) The interval dominance discernibility matrix based on β-IFDNRS is developed for feature selection and can generate a set of complete and diverse feature subsets. (iii) The novel interval-valued fuzzy monotonic decision tree (IFMDT) based on the probability distribution is trained on each feature subset and is used as the base classifier of the weighted voting fusion model. Extensive experiments show the proposed fusion learning method has significant advantages.

A large-scale group decision-making method based on group-oriented rough dominance relation in scenic spot service improvement

In today’s age of big data and information, large-scale group decision-making has become an essential aspect of modern economy, science, and technology. This paper proposes a large-scale group decision-making method that leverages group-oriented rough dominance relation to identify the worst group when addressing complex issues that involve a large number of decision-makers. The proposed method entails building a set-valued ordered information system that utilizes clustering learning to reduce data dimensions and reduce the dimensions of decision space, thereby improving the efficiency of the decision-making process. Additionally, it proposes a novel group-oriented rough dominance relation based on dominance-based rough set theory. By clarifying this advantage relationship, more targeted focus is placed on the group that needs improvement, thereby improving decision-making effectiveness. The proposed method calculates the advantage degree of alternative group plans to select the worst group. The main purpose is to compare the advantages and disadvantages of different groups by defining a metric that quantifies the rough dominance relation between groups, thereby improving the objectivity and repeatability of the decision-making process. Finally, the study applies the proposed method to a case study aimed at improving various types of services in European scenic spots, and the benefits of the method are discussed. Experimental analysis shows that the method in this study can screen the groups that should be improved most in this case, showing the benefits and applicability of the proposed method, and providing valuable insights for complex decision-making problems involving multiple decision-makers.

Criteria Selection of Housing Loan Based on Dominance-Based Rough Set Theory: An Indian Case

Because India has one of the world’s fastest-growing economies, the Indian banking sector is essential to the country’s reform. The approval of home loans to customers is one of the crucial tasks carried out by Indian banks. The risk of loan repayment outside of the agreed-upon time frame can be reduced by accurately estimating the customer’s loan need. The majority of earlier studies on the development of banking lacked a methodical approach to analyze qualitative data. Even though the traditional multivariate statistically based factor analysis approach is a great way to categories data in qualitative analysis, the technique cannot be used without any statistical presumptions and additional information about the data. This study handles the banking attributes related to home loans using the Dominance-based Rough Set Approach (D-RSA). In order to categorize the customer’s attributes, this study suggests using a preference-based “if … then” decision rule. This rule can aid decision makers in understanding the risk factors associated with loan factors for a financial organization.

Feature Selection Considering Multiple Correlations Based on Soft Fuzzy Dominance Rough Sets for Monotonic Classification

Monotonic classification is a common task in the field of multicriteria decision-making, in which features and decision obey a monotonic constraint. The dominance-based rough set theory is an important mathematical tool for knowledge acquisition in monotonic classification tasks (MCTs). However, existing dominance-based rough set models are very sensitive to noise information, and only a misclassified sample will lead to large errors in acquiring knowledge. This unstable phenomenon does not meet the requirements of practical applications. On the other hand, feature selection is supposedly an effective dimensionality reduction approach for classification tasks. In the real world, feature combinations with multiple correlations can often provide important classification information, where the multiple correlations include redundancy, complementarity, and interaction between features. To the best of our knowledge, most of the existing feature selection methods for MCTs only consider the relevance between features and decision, while ignoring the multiple correlations. To overcome these two drawbacks, in this article, we propose a robust fuzzy dominance rough set model, and develop a feature selection method that considers multiple correlations based on the robust model for MCTs. First, a soft fuzzy dominance rough set (SFDRS) with robustness is proposed. Second, a feature evaluation index considering multiple correlations is presented. Finally, a feature selection algorithm based on SFDRS is designed to select an optimal feature subset. Extensive experiments are conducted on 12 public datasets, and the results show that the SFDRS model has good robustness and the proposed feature selection algorithm has excellent classification performance.

Criteria selection and decision making of hotels using Dominance Based Rough Set Theory

Accommodation is one of the necessities of tourists and travel agencies' significant responsibilities. With the growing competition and profit-making various tour organising companies have started providing attractive accommodation options to the travellers to win their choices. Present research performs a case study on accommodation providing hotels through designing a strategy to enhance their profit earrings by welcoming more and more tourists. The methodology comprises rough set theory (RST) using the Dominance Based rough set theory (DRST) on the collected data of selected variables such as location, facility, value for money, etc., of hotels. Correspondingly, if and then decision rule has been used to classify these variables. The statistical methods regression analysis has also been used to define each variable's relationship and influence on concerned authorities' decision-making. The results show that hotels and tourists can benefit from the proposed strategy and help in decision making by understanding tourist behaviour, increasing profit, improving services, and quality of hotels.

The use of rough rules in the selection of topographic objects for generalizing geographical information

Abstract Selection is a key element of the cartographic generalisation process, often being its first stage. On the other hand it is a component of other generalisation operators, such as simplification. One of the approaches used in generalization is the condition-action approach. The author uses a condition-action approach based on three types of rough logics (Rough Set Theory (RST), Dominance-Based Rough Set Theory (DRST) and Fuzzy-Rough Set Theory (FRST)), checking the possibility of their use in the process of selecting topographic objects (buildings, roads, rivers) and comparing the obtained results. The complexity of the decision system (the number of rules and their conditions) and its effectiveness are assessed, both in terms of quantity and quality – through visual assessment. The conducted research indicates the advantage of the DRST and RST approaches (with the CN2 algorithm) due to the quality of the obtained selection, the greater simplicity of the decision system, and better refined IT tools enabling the use of these systems. At this stage, the FRST approach, which is characterised by the highest complexity of created rules and the worst selection results, is not recommended. Particular approaches have limitations resulting from the need to select appropriate measurement scales for the attributes used in them. Special attention should be paid to the selection of network objects, in which the use of only a condition-action approach, without maintaining consistency of the network, may not produce the desired results. Unlike approaches based on classical logic, rough approaches allow the use of incomplete or contradictory information. The proposed tools can (in their current form) find an auxiliary use in the selection of topographic objects, and potentially also in other generalisation operators.

The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement).

Defining Poverty Using Dominance-Based Rough Set Theory and Proposing Strategic Objectives for the United Nations Developing Countries

This article is the last of a series of three researches. The purpose of this research is to expose the results of using Dominance-based Rough Set Approach (DRSA) to help International organizations (both non-governmental organizations and governmental organizations) define poverty, identifying economical, sociological, political and technological strategic objectives for developing countries. More precisely, politicians, decision makers and international organizations will be able to study 23 various political, economical, sociological and technological indicators and classify all the countries according to the following three different categories: [A] Countries that are doing well according to the selected indicators; [B] Countries that need support to acquire category A status; [C] Countries ranked the lowest and meeting special support with regard to the criterion or criteria considered. The three categories are delimited by tertiles relative to the average ranking of the member states of the United Nations. The chosen criteria are measured in order to provide decision rules based on this classification. These decision rules thus focus on the strategic needs of countries with respect to improving their development and classification. We strongly believe that by targeting these identified needs, this research will help the sustainable development of countries in need to set realistic targets, prioritize International funding, evaluate economical growth and sociological improvements. Among the results of this article, priorities for countries ranked the lowest should focus on reducing adolescent fertility and increasing school life expectancy.

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.

Rock burst laws in deep mines based on combined model of membership function and dominance-based rough set

Rock bursts are spontaneous, violent fracture of rock that can occur in deep mines, and the likelihood of rock bursts occurring increases as depth of the mine increases. Rock bursts are also affected by the compressive strength, tensile strength, tangential strength, elastic energy index, etc. of rock, and the relationship between these factors and rock bursts in deep mines is difficult to analyze from quantitative point. Typical rock burst instances as a sample set were collected, and membership function was introduced to process the discrete values of these factors with the discrete factors as condition attributes and rock burst situations as decision attributes. Dominance-based rough set theory was used to generate preference rules of rock burst, and eventually rock burst laws analysis in deep mines with preference relation was taken. The results show that this model for rock burst laws analysis in deep mines is more reasonable and feasible, and the prediction results are more scientific.

The Application of Dominance-based Rough Sets Theory to Evaluation of Transportation Systems

The paper presents an original procedure of evaluation of a transportation system, resulting in its assignment into a predefined class, representing the overall standard of the considered system and the level of transportation service. The method relies on the application of the dominance-based rough set theory (DRST), allows for thorough data exploration, evaluation of informational content of the considered characteristics and generation of certain decision rules that support t he evaluation process. In the analysis different characteristics (criteria and attributes) describing various aspects of a transportation system operations are taken into account. The assignment of a transportation system to a specific quality class is performed based on the values of characteristics which are compared with the evaluation pattern, i.e. the set of decision rules generated through the analysis of customers’ opinions and expectations concerning a transportation system. The method is composed of three major steps, including: 1) identification of the most important characteristics, 2) generation of the evaluation pattern, and 3) assignment of the transportation system to the appropriate class. In the evaluation process five key components of a transportation system, including: transportation means, human resources, informational resources, transportation infrastructure and technical equipment as well as organizational rules are considered.

A rough set based decision support approach to improving consumer affective satisfaction in product design

Affective design, also known as Kansei Engineering, is an effective methodology for the study of interactions between human affections and design of products, so as to improve consumer satisfaction. One challenging issue of affective design is to discover the mapping pattern between consumer affections and product design elements from raw design data, which are usually characterised by non-linearity and uncertainty. In view of the insufficiency of traditional methods in dealing with such an issue, this paper proposes a new decision support approach that is based on the principles of dominance-based rough set theory for the study of interactions between consumer affective needs and product features in product design. The proposed approach is able to handle imprecise consumer perceptions and discover latent patterns to guide affective design. The result obtained from an illustrative example shows that the proposed approach provides an effective means to facilitate the affective mapping process and thus is able to improve consumer affective satisfaction with product design. Relevance to industry This paper proposes an explicit decision support approach to facilitate the affective design mapping process and incorporate consumer affective needs into product design elements, so as to improve consumer affective satisfaction with product design.

A dominance-based rough set approach to Kansei Engineering in product development

Keen competitions in the global market have led product development to a more knowledge-intensive activity than ever, which requires not only tremendous expert knowledge but also effective analysis of design information. Kansei Engineering as a customer-oriented methodology for product development, often has to analyse imprecise design information inherent with nonlinearity and uncertainty. This paper proposes a systematic approach to Kansei Engineering based on the dominance-based rough set theory. Two novel concepts known as category score and partition quality have been developed and incorporated into the proposed approach. The new approach proposed is able to identify and analyse two types of inconsistencies caused by indiscernibility relations and dominance principles respectively. The result of an illustrative case study shows that the proposed approach can effectively extract Kansei knowledge from imprecise design information, and it can be easily integrated into an expert system for customer-oriented product development.