Dominance-based Rough Set Approach Research Articles

Kansei evaluation for group of users: A data-driven approach using dominance-based rough sets

Kansei refers to people’s subjective feeling and impression. Kansei evaluation devotes to assessing users’ preferences for product items according to multiple Kansei attributes, thus supporting the decision making of consumers and/or designers. The objective of this paper is to propose a data-driven approach for addressing user group oriented Kansei evaluation. The approach consists of three phases. The first phase identifies the representative Kansei attributes and product samples of the product domain to gather exemplary evaluation dataset from sampled representative users. In light of the specified Kansei need and relying on the dominance-based rough set approach, the second phase constructs the collective decision table so as to further infer the collective preferential information in terms of dominance-based decision rules and Kansei importance weights. The third phase presents a two-step sequential heuristic model for characterizing users’ affective preference behavior: (1) a multicriteria classifier using dominance-based decision rules for product sorting, partly simulating the satisficing heuristic; and (2) a simple choice strategy for product ranking, manifesting the CONF heuristic. A case study involving the toaster domain was conducted to verify the proposed approach. The theoretical and practical implications of the proposed approach are also discussed.

Fuzzy extensions of the dominance-based rough set approach

In this paper, we first review existing fuzzy extensions of the dominance-based rough set approach (DRSA), and advance the theory considering additional properties. Moreover, we examine the application of Ordered Weighted Average (OWA) operators to fuzzy DRSA. OWA operators have shown a lot of potential in handling outliers and noisy data in decision tables, when they are combined with the indiscernibility-based rough set approach (IRSA). We examine theoretical properties of the proposed hybridisation of OWA operators with fuzzy DRSA. At the end, we experimentally compare the robustness of the standard fuzzy DRSA approach with the OWA one.

Incremental attribute reduction approaches for ordered data with time-evolving objects

Dominance-based rough set approach (DRSA) is widely applied to multi-criteria decision analysis and sorting problems for data with preference-ordered relation, where attribute reduction is an important research field. At present, based on DRSA, many traditional attribute reduction approaches are extended to process a static ordered data. In real-world applications, ordered data with time-evolving objects widely exist, which is called a dynamic ordered data. However, for dynamic ordered data, employing these existing approaches to compute reducts is very time-consuming, since they need to recalculate knowledge from scratch when multiple objects vary. Incremental updating method can effectively complete the dynamic learning task, because it can acquire new knowledge based on previous knowledge. Inspired by this, this work studies incremental attribute reduction approaches for dynamic ordered data in DRSA framework. First, matrix-based method for calculating the dominance conditional entropy is investigated. Next, the updating principles of the dominance relation matrix and dominance diagonal matrix are studied when objects vary. Finally, two incremental algorithms of attribute reduction are proposed when multiple objects are added to or deleted from an ordered decision system, respectively. Experiments on different datasets provided by University of California at Irvine (UCI) are conducted to evaluate the proposed algorithms. Experimental results show that the proposed incremental algorithms can effectively and efficiently accomplish the task of attribute reduction in dynamic ordered data.

Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process.

Multiple-unit pellet systems (MUPS) offer many advantages over conventional solid dosage forms both for the manufacturers and patients. Coated pellets can be efficiently compressed into MUPS in classic tableting process and enable controlled release of active pharmaceutical ingredient (APIs). For patients MUPS are divisible without affecting drug release and convenient to swallow. However, maintaining API release profile during the compression process can be a challenge. The aim of this work was to explore and discover relationships between data describing: composition, properties, process parameters (condition attributes) and quality (decision attribute, expressed as similarity factor f2) of MUPS containing pellets with verapamil hydrochloride as API, by applying a dominance-based rough ret approach (DRSA) mathematical data mining technique. DRSA generated decision rules representing cause–effect relationships between condition attributes and decision attribute. Similar API release profiles from pellets before and after tableting can be ensured by proper polymer coating (Eudragit® NE, absence of ethyl cellulose), compression force higher than 6 kN, microcrystalline cellulose (Avicel® 102) as excipient and tablet hardness ≥42.4 N. DRSA can be useful for analysis of complex technological data. Decision rules with high values of confirmation measures can help technologist in optimal formulation development.

An optimized method to calculate approximations in Dominance based Rough Set Approach

Classical Rough Set Theory (RST) is a prominent tool to deal with uncertainty of categorical data. However, it does not consider the preference order between the values of the attributes. Dominance Based Rough Set Approach (DRSA) provides dominance relation in this regard. Computation of the upper and lower approximation is a critical step in DRSA. However, computing these approximations is a computationally expensive task. Efficiently computing approximations will thus be helpful in reducing the execution time of algorithms using these approximations. In this paper, we have proposed an efficient approach to compute these measures. The proposed approach directly calculates approximations without considering the objects that do not play any role in the approximations. In our approach, one instance of a dataset is compared with another instance only once which avoids unnecessary comparisons. The proposed approach is compared with the conventional method using sixteen benchmark data sets from UCI. Results show that the proposed approach significantly reduces the execution time. The average reduction in the execution time was found to be almost 85%. This approach also reduces the memory consumption by 75%. The Big-O complexity is also reduced. These measures justify that the proposed approach is more effective and efficient as compared to the conventional DRSA.

Auto loan fraud detection using dominance-based rough set approach versus machine learning methods

Financial fraud is escalating as financial services and operations grow. Despite preventive actions and security measures deployed to mitigate financial fraud, fraudsters are learning and finding new ways to get around fraud prevention systems, thereby, challenging quantitative techniques and predictive models. Thus, new techniques must be explored and tested so the insights obtained from the analysis may be used to support more accurate fraud prediction and the development of fraud prevention systems which have additional checks to mitigate suspicious events. Auto loan is a significant financial product not yet explored in the literature, unlike the misuse of credit cards, for instance. Given the recent increase in fraudulent transactions concerning auto loan applications, this paper tests a new data set for auto loan applications using a technique not yet explored for financial fraud prediction, namely the Dominance-based Rough Set Balanced Rule Ensemble (DRSA-BRE), and after comparing it with other techniques traditionally used for predicting financial fraud, finds that the proposed approach has several advantages over the traditional ones.

Double-quantitative variable consistency dominance-based rough set approach

Rough set model with double quantification satisfies the requirement of quantitative information in practical applications, it has better fault tolerance than probabilistic rough set model considering only relative quantification and graded rough set model considering only absolute quantification. In this paper, two kinds of consistency levels are introduced from the perspective of double quantification in an ordered information system, namely relative quantitative consistency level and absolute quantitative consistency level. The single-quantitative variable consistency dominance-based rough set models based on these two kinds of quantitative consistency levels and their basic properties with the relevant three-way decision rules are discussed respectively in an ordered information system. Moreover, two kinds of double-quantitative variable consistency dominance-based rough set models and their basic properties with the relevant decision rules based on these two kinds of quantitative consistency levels are introduced. A consistency analysis of decision making in a practical case study is used to illustrate and interpret the double-quantitative variable consistency rough set models and the related decision rules in the ordered information system. The obvious shortcomings of dominance-based rough set approach (DRSA) without quantitative information are compared to explain the advantages of the quantitative variable consistency dominance-based rough sets with the two consistency levels in the practical case study.

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk.

Accounting for about 290,000-650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

Targeting Poverty and Developing Sustainable Development Objectives for the United Nation’s Countries using a Systematic Approach Combining DRSA and Multiple Linear Regressions

The objectives of this article is to target poverty using Dominance-based Rough Set Approach (DRSA) to help the United Nation’s Countries develop objectives for sustainable development. There are 12 variables divided into 2 perspectives. The first is an economical and technological perspective composed of 6 variables. The second is a sociological and political perspective composed of 6 variables. The methodology proposed classifies all the United Nation’s countries according to three different categories: [A] Developed countries; [B] Emerging economies that need support to acquire category A status; [C] Under Developed countries ranked the lowest and needing special support with regard to the criterion or criteria considered. Using this classification, DRSA provides decision rules to explain the classification and indicating precisely what are the conditions to be part of a higher category. Also, the results indicate what are the conditions to be part of the Under Developed countries category and therefore helps targeting poverty and proposing, at the same time, objectives to improve this classification. Finally, we used Multiple Linear Regressions with selected decision rules to test selected decision rules as the Gross National Income per capita as the dependent variable.

A multicriteria approach based on rough set theory for the incremental Periodic prediction

This paper proposes a Multicriteria Approach for the Incremental Periodic Prediction (MAI2P). This approach is periodically applied while considering the sequential evolution of the dynamic information system under the variation of the set of actions in an ever-evolving learning sample. It is based on the Dominance-based Rough Set Approach (DRSA) and consists of three phases. The first aims at constructing a decision table and is based on three steps: (1) constructing a representative learning sample of “Actions of reference”, (2) constructing a coherent criteria family for the actions’ characterization and (3) building a decision table. The second consists in an incremental updating of the DRSA approximations in order to infer a preference model resulting in a set of decision rules. The third consists of classifying the potential actions in one of the predefined decision classes. The first two phases run at the end of the current period and the third phase runs at the beginning of the next period. The approach MAI2P has been applied in the context of Massive Open Online Courses (MOOCs). It has been validated on a French MOOC proposed by a Business School in France. Experiments showed that the pessimistic cumulative approach gives the most efficient preference model with an F-measure and an accuracy values reaching 0.66 and 0.89 respectively.

Insects as Novel Food: A Consumer Attitude Analysis through the Dominance-Based Rough Set Approach.

In Western societies, the unfamiliarity with insect-based food is a hindrance for consumption and market development. This may depend on neophobia and reactions of disgust, individual characteristics and socio-cultural background, and risk-perceptions for health and production technologies. In addition, in many European countries, the sale of insects for human consumption is still illegal, although European Union (EU) and the European Food Safety Authority (EFSA) are developing regulatory frameworks and environmental and quality standards. This research aims to advance the knowledge on entomophagy, providing insights to improve consumer acceptance in Italy. This is done by carrying out the characterization of a sample of consumers according to their willingness to taste several types of insect-based food and taking into account the connections among the consumers’ features. Thus, the dominance-based rough set approach is applied using the data collected from 310 Italian consumers. This approach provided 206 certain decision rules characterizing the consumers into five groups, showing the consumers’ features determining their specific classification. Although many Italian consumers are willing to accept only insects in the form of feed stuffs or supplements, this choice is a first step towards entomophagy. Conversely, young Italian people are a niche market, but they can play a role in changing trends.

Sovereign Rating Analysis through the Dominance-Based Rough Set Approach

Abstract The classifications of risk made by international rating agencies aim at guiding investors when it comes to the capacity and disposition of the evaluated countries to honor their public debt commitments. In this study, the analysis of economic variables of sovereign rating, in a context of vagueness and uncertainty, leads the inference of patterns (multi-criteria rules) by following the Dominance-based Rough Set Approach (DRSA). The discovery of patterns in data may be useful for subsidizing foreign investment decisions in countries; and this knowledge base may be used in rule-based expert systems (learning from training examples).The present study seeks to complement the analysis produced by an international credit rating agency, Standard & Poor’s (S&P), for the year 2018.

Identifying areas vulnerable to homicide using multiple criteria analysis and spatial analysis

The decision-making process in public security is not an easy task. Several aspects must be considered, since resources are limited, whereas coverage should be extensive. Usually, preventative actions are allocated to areas that are more prone to violence, where criminal occurrences have happened in the recent past. In Brazil, such decisions are made in an ad-hoc way, considering only the knowledge of a specialist. This paper, however, aims to identify homicide vulnerability areas, taking into account the knowledge and preferences of an expert decision-maker under several criteria and data from a demographic census. Our model aggregates multiple-criteria analysis, based on Dominance-based Rough Set Approach, and spatial analysis, which consist of hot-spot analysis and local Moran's I. The model was applied in a neighbourhood of Brazil. The approach was able to highlight the problematic areas in the neighbourhood, and suggested locations where public policy and, consequently, limited resources should be allocated.

Relationships among sustainability dimensions: evidence from an Alpine area case study using Dominance-based Rough Set Approach

The study provides a useful tool to enable local decision makers to define territorial sustainable policies. For this purpose, using selected indicators, we analyse, at the municipal level, the behaviour of and interactions among the socio-demographic, environmental, economic, and accessibility dimensions in two northern Italian provinces in the Alpine Chain (NUTS3), which are identified by the EU as predominantly rural areas. We adopt the dominance-based rough set approach, which allows for the discovery of hidden patterns among the data by means of the generation of decision rules. The population variation over time and number of foreign citizens are the decision attributes considered together with condition attributes belonging to the socio-demographic, environmental, economic, and accessibility dimensions. Our selected areas (rural, mountainous, and peripheral) present various facets at the sustainability level, largely due to their complexity, which was confirmed by the rules we found. Moreover, the socio-demographic and accessibility attributes are more commonly present in the rules and a low interaction characterizes the four dimensions. Finally, economic growth is often at odds with environmental sustainability.

Data meaning and knowledge discovery: Semantical aspects of information systems

Data tables provide the standard means of representation of qualitative or quantitative information about objects of interest. They also form a starting point for the task of information processing: an operation of passing from raw data or information to semantically processed knowledge. The fundamental issue here is the question about the meaning of data: What do entries in the data table actually tell us about objects? It entails another question: How should the meaning be further processed? The primary aim of the article is an attempt to answer these two questions. To this end we are going to employ conceptual scales from formal concept analysis, an important theory of data processing introduced by Rudolf Wille, and apply them to data tables so as to obtain multivalued information systems, which were introduced and developed within the conceptual framework of rough set theory by Zdzisław Pawlak and Ewa Orłowska. Our main idea is to regard multivalued information systems as the semantics or meaning of the original tables. This idea allows us to describe and combine classical rough set theory, dominance-based rough set approach, and formal concept analysis within the single framework of multivalued information systems, which is rich enough to cope with a number of semantical nuances that may occur during the process of data analysis.

COMPLEMENTARITY OF THE GRAPHICAL ANALYSIS FOR INTERACTIVE AID AND DOMINANCE-BASED ROUGH SET APPROACH APPLIED TO THE CLASSIFICATION OF NON-URBAN MUNICIPALITIES

Graphical analysis for interactive aid (GAIA) and the dominance-based rough set approach (DRSA) are compared as methods of explaining the solution to a multi-criteria ranking problem obtained using the preference ranking organization method for the enrichment of evaluations (PROMETHEE). The classification of 52 municipalities in Northern Quebec in terms of the socioeconomic situation is based on three attributes: home conditions, employment and demographic potential. The classification provided to the decision maker is aggregated information. To facilitate decision-making, the problem is first considered as a sorting task, in which municipalities are distributed into three categories: best (B), worst (W) or intermediate (I), based on the PROMETHEE ranking. In order to improve the position of a municipality thus categorized, the decision maker needs information that will answer the questions: What criteria are relevant to the municipality? What criteria are in conflict? What are the critical values of the criteria? We show that GAIA and DRSA provide convergent and complementary information that allow enrichment of the answers to these questions. Keywords: management decision support, multi-criteria analysis, GAIA, dominance-based rough set approach.

Evaluation of and improvement planning for smart homes using rough knowledge-based rules on a hybrid multiple attribute decision-making model

This paper proposes fuzzy integral-based decision methods to identify the core factors and their relationships for smart homes product improvement. The dominance-based rough set approach was used to retrieve core attributes and obtain rough knowledge-based rules. The decision-making trial and evaluation laboratory (DEMATEL) technique was used to build an influential network relationship map, and influential weights were determined through the DEMATEL-based analytic network process. Subsequently, the inter-relationships among criteria were calculated. Finally, the fuzzy integral method was used to measure the plausible synergy effects among the criteria, evaluate/rank alternatives for smart homes, and then provide suggestions for product improvement. The main innovation is the use of rough knowledge-based rule retrieval procedures and fuzzy measures for exploring the synergy effects on smart home improvement. Three smart home products/systems were examined to illustrate their performance on each criterion for improvement planning. This study contributes knowledge to research on consumer adoption of smart homes and presents improvement strategies.

A novel approach for efficient updating approximations in dynamic ordered information systems

Dynamic data in real-time application are typically updating in a multi-dimensional manner. In this paper, we introduce a novel approach based on Dominance-based Rough Set Approach (DRSA) to efficiently deal with the multi-dimensional variations of attribute set and attribute values in dynamic Ordered Information Systems (OIS). We improve the original notion of the P-generalized decision domains to make the feature value matrix be dominance symmetrical, and propose an efficient strategy based on the improved notion to obtain the dominance feature matrix. Then, we employ the dominance-feature-matrix-based incremental strategy to avoid repeated comparisons between original attributes, so that to efficiently update rough approximations of DRSA with the simultaneously increased attribute set and varied attribute values. In our approach, the steps based on these two combined strategies can work altogether or separately, not only efficiently dealing with the simultaneously increased attribute set and varied attribute values, but also efficiently dealing with the individually increased attribute set or varied attribute values in dynamic OIS. Efficient algorithm based on the updating strategies is designed and multiple groups of experiments are conducted. Experimental results on different real-world data sets show that the proposed algorithm is much faster than other algorithms for dealing with the multi-dimensional or the single-dimensional variations of attribute set and attribute values.

A parallel approach to calculate lower and upper approximations in dominance based rough set theory

Feature selection plays an important role in data mining and machine learning tasks. Rough set theory has been a prominent tool for this purpose. It characterizes a dataset by using two important measures called lower and upper approximation. Dominance based rough set approach (DSRA) is an extension to conventional rough set theory. It is based on persistence of preference order while extracting knowledge from datasets. Dominance principal states that objects belonging to a certain decision class should follow the preference order. Preference order states that an object having higher values of conditional attributes should belong to higher decision classes. However, some of the basic concepts like checking preference order consistency of a dataset, dominance based lower approximation and upper approximation are computationally too expensive to be used for large datasets. In this paper, we have proposed a parallel incremental approach called Parallel Incremental Approximation Calculation or PIAC for short, for calculating these measures of lower and upper approximations. The proposed approach incrementally calculates lower and upper approximations using parallel threads. We compare our method with the conventional approach using ten widely used datasets. Whilst achieving the same accuracy levels as the conventional approach, our approach significantly reduces the average computation time, i.e., 71% for the lower approximation and 70% for the upper approximation. Over all datasets, the decrease in memory usage achieved was 99%.

Dynamic dominance rough set approach for processing composite ordered data

Dominance-based Rough Set Approach (DRSA) is a significant extended rough set model to process multi-criteria classification problems in which there needs to be consideration of the preference order between different objects. It can handle a special type of attributes with preference-ordered domains by a dominance relation. This article mainly focuses on employing the DRSA to process composite ordered decision systems including categorical, numerical, set-valued, interval-valued and missing attributes, and dynamic maintenance of the lower and upper approximations under the attribute generalization. Firstly, we introduce a new composite dominance-based rough set model in which the composite dominance relation may not satisfy three basic properties, i.e., reflexivity, symmetry and transitivity. Based on the proposed relation, we redefine the lower and upper approximations of upward and downward unions of decision classes. Then, we present matrix-based methods for calculating approximations through introducing the dominant (dominated) matrix and other induced matrices. In addition, we put forward the matrix-based incremental approaches for the update of approximations in composite ordered decision systems while the attribute set varies with time, which can avoid reconstructing these matrices from scratch. Finally, according to a series of comparative experimental results, it can easily be observed that our proposed incremental updating methods have a great advantage in improving the computational performance for dynamic attribute generalization.