Variable precision rough set model for attribute selection on environment impact dataset

The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment

A Rule-based Service Customization Strategy for Smart Home Context-aware Automation

The continuous technical progress of the smartphone built-in modules and embedded sensing techniques has created chances for context-aware automation and decision support in home environments. Studies in this area mainly focus on feasibility demonstrations of the emerging techniques and system architecture design that are applicable to the different use cases. It lacks service customization strategies tailoring the computing service to proactively satisfy users’ expectations. This investigation aims to chart the challenges to take advantage of the dynamic varying context information, and provide solutions to customize the computing service to the contextual situations. This work presents a rule-based service customization strategy which employs a semantic distance-based rule matching method for context-aware service decision making and a Rough Set Theory-based rule generation method to supervise the service customization. The simulation study reveals the trend of the algorithms in time complexity with the number of rules and context items. A prototype smart home system is implemented based on smartphones and commercially available low-cost sensors and embedded electronics. Results demonstrate the feasibility of the proposed strategy in handling the heterogeneous context for decision making and dealing with history context to discover the underlying rules. It shows great potential in employing the proposed strategy for context-aware automation and decision support in smart home applications

A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes

Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support

Klasifikasi Kompetensi Jabatan Pada Pegawai Negeri Sipil (PNS) Dalam Jabatan Fungsional Umum (JFU) Menggunakan Metode Multi Rough Set

Pada instansi pemerintah, seorang Pegawai Negeri Sipil (PNS) dituntut harus memiliki kompetensi atau kemampuan untuk dapat melakukan pekerjaan secara efektif dan efisien sesuai dengan bidang dan lingkup pekerjaannya. Pada kenyataannya, proses penentuan nama jabatan dan penempatan bagi Pejabat Fungsional Umum masih dilakukan secara manual, sehingga membutuhkan waktu yang cukup lama dan hasil yang diperoleh belum tentu akurat sesuai dengan kompetensi yang dimiliki. Pada penelitian ini, Metode Multi Rough Set digunakan dalam penentuan klasifikasi kompetensi jabatan bagi PNS yang belum diketahui kompetensinya maupun sebagai bahan evaluasi kinerja pegawai yang telah menduduki suatu jabatan. Metode Multi Rough Set ini dilakukan dengan cara membagi data set menjadi beberapa data set dengan atribut yang sejenis. Berdasarkan penelitian yang telah dilakukan, diketahui bahwa tingkat akurasi hasil klasifikasi dengan Metode Multi Rough Set meningkat lebih baik dibandingkan dengan Metode Single Rough Set yaitu dari tingkat akurasi 53.85% correct, 26.92% incorrect dan 19.23% unclassified, meningkat menjadi 57.14% correct, 42.86% uncorrect dan 0% unclassified, disamping itu Metode Multi Rough Set mempunyai luas daerah di bawah kurva berdasarkan hasil kurva Reveiver Operating Characteristic (ROC) yaitu sebesar 0.866 sehingga dapat dikatakan bahwa Metode Multi Rough Set sebagai metode klasifikasi yang baik (Good Classifier) untuk penentuan klasifikasi kompetensi jabatan pada Pegawai Negeri Sipil (PNS) dalam Jabatan Fungsional Umum (JFU). ========================================================= In government agencies, a Civil Servantsis required to have the competency or the ability to finish the work effectively and efficiently in accordance with the field and scope of work. In fact, a process of determining positions and placements for a functional worker is still be done manually, thus, it takes delay. Moreover, its obtained results are not totally accurate regarding with their competencies. In this research, Multi Rough Set Method was used to determine Civil Servant’s classification of whose positions were still undecided, and as an evaluation of employee’s competency who have occupied a position as well. Multi Rough Set Method was applied by dividing data set into several data sets with similar attributes. The result of this research was showing that the accuracy rate of Multi Rough Set Method is used and it’s combined with Fuzzy Rule Set. It has shown that final decision result in Multi Rough Set is higher than Single Rough Set Method. The previous accuracy rate was shown as 38.78% correct, 32.65% incorrect and 28.57% unclassified, then it’s increased to 57.14% correct, 42.86% incorrect and 0% unclassified, beside that Multi Rough Set Method has Area Under Cover (AUC) based on Receiver Operating Characteristic (ROC) Curve Result that is 0.866, so it can be concluded that Multi Rough Set Method is a Good Classifier for Job-Competency in Functional Works Classification Of Civil Servants decision-making

Sistem Pengambilan Keputusan dalam Penentuan Kelas Jabatan Fungsional Umum (JFU) Pegawai Negeri Sipil (PNS) Menggunakan Metode Multi Rough Set dan Fuzzifikasi

Seorang Pegawai Negeri Sipil (PNS) pada instansi pemerintah, dituntut harus memiliki kompetensi atau kemampuan untuk dapat melakukan pekerjaan secara efektif dan efisien sesuai dengan bidang dan lingkup pekerjaannya. Pada kenyataannya, proses penentuan kompetensi dan kelas jabatan sangat berpengaruh bagi proses penempatan Jejabat Fungsional Umum (JFU) seorang Pegawai Negeri Sipil dan karena proses tersebut selama inimasih dilakukan secara manual, maka waktu yang dibutuhkan cukup lama dan hasil yang diperoleh belum tentu akurat sesuai dengan kompetensi yang dimiliki. Pada penelitian ini, Metode Multi Rough Set digunakan dalam penentuan klasifikasi kompetensi dan kelas jabatan bagi PNS yang belum diketahui kompetensinya maupun sebagai bahan evaluasi kinerja pegawai yang telah menduduki suatu jabatan. Metode Multi Rough Set  ini dilakukan dengan cara membagi data set menjadi beberapa data set dengan atribut yang sejenis. Berdasarkan penelitian yang telah dilakukan, dapat diketahui bahwa Metode Multi Rough Set sebagai metode klasifikasi yang baik (Good Classifier) dalam pengambilan keputusan klasifikasi kompetensi pegawai dalam Jabatan Fungsional Umum, karena berdasarkan hasil kurva pada Receiver Operating Characteristic (ROC) mempunyai luas daerah di bawah kurva sebesar 0,866, selain itu rata-rata error dari hasil klasifikasi dengan Metode Multi Rough Set yang digabungkan dengan pengambilan keputusan melalui fuzzifikasi meningkat secara signifikan dibandingkan dengan Metode Single Rough Set yaitu dari 28,75% menjadi 0% untuk hasil yang tidak terklasifikasi.AbstractA Civil Servant in government agencies is required to have the competency or ability to be able to perform work effectively and efficiently in accordance with the field and scope of work. In fact, the process of determining the competency and class of works is very influential for the process of placement of General Functional Works of a Civil Servant. However, the process takes a long time because it is still done manually.  Moreover, the obtained results are not necessarily accurate in accordance with the competence which is owned by the civil servants. In this study, Multi Rough Set Method is used for determining unknown civil servants competency classification and class position, or as civil servants performance evaluation. The multi Rough Set method is applied by dividing the data set into several similar attributes data sets. Based on the research that has been conducted, it can be seen that the Multi Rough Set Method is a good classifier method in decision making of employee competency classification in General Functional Work. It is because based on the Receiver Operating Characteristic (ROC) curve results, the area under the curve reaches 0.866. Besides, the average error from the results of the classification using the combination of Multi Rough Set Method and fuzzification increased significantly compared to the Single Rough Set Method which goes from 28.75% to 0% for unclassified results

A new efficiency evaluation approach with rough data: An application to Indian fertilizer

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the world of chaos, nothing is certain. In such an unpredictable world, measuring the efficiency of any individual is inevitable. In a conventional data envelopment analysis (DEA) model, exact input and output quantity data are needed to measure the relative efficiencies of homogeneous decision-making units (DMUs). However, in many real-world applications, the exact knowledge of data might not be available. The rough set theory allows for handling this type of situation. This paper tries to construct a rough DEA model by combining conventional DEA and rough set theory using optimistic and pessimistic confidence values of rough variables, all of which help provide a way to quantify uncertainty. In the proposed method, the same set of constraints (production possibility sets) is employed to build a unified production frontier for all DMUs that can be used to properly assess each DMU's performance in the presence of rough input and output data. Besides, a ranking system is presented based on the approaches that have been proposed. In the presence of uncertain conditions, this article investigates the efficiency of the Indian fertilizer supply chain for over a decade. The results of the proposed models are compared to the existing DEA models, demonstrating how decision-makers can increase the supply chain performance of Indian fertilizer industries

Modeliranje tehnoloških procesa u rudarstvu u uslovima nedovoljnosti podataka primenom teorije grubih skupova

Rudarstvo, a u okviru njega i priprema mineralnih sirovina, se odlikuje složenošću tehnoloških procesa što je posledica velikog broja uticajnih parametara. Samim tim je potrebno biti veoma obazriv prilikom donošenja odluka u oblasti rudarstva. U cilju što efikasnijeg funkcionisanja procesa, moguće je primeniti različite metode koje služe za pojednostavljenje procesa odlučivanja. Jedna od takvih metoda jeste i teorija grubih skupova. Ona predstavlja relativno novu matematičku teoriju koja je pogodna za razumevanje nepreciznih i nepotpunih podataka kao i za otkrivanje međusobnih odnosa između tih podataka. Teorija grubih skupova je pronašla primenu u različitim granama industrije, međutim pregledom literature došlo se do zaključka da primenjivost ove teorije u rudarstvu nije dovoljno ispitana. To je bila polazna osnova doktorske disertacije, ispitivanje mogućnosti njene primene u oblasti rudarstva. U okviru eksperimentalnog dela disertacije izvršeno je testiranje mogućnosti primene teorije grubih skupova za rešavanje tri problema: izbor lokacije za flotacijsko jalovište, izbor flotacijskog kolektora i izbor flotacione mašine. Određeni su parametri koji imaju najviše uticaja prilikom ova tri izbora, odnosno određeni su kriterijumi za izbor. Izabranim kriterijumima su dodeljene odgovarajuće vrednosti, a nakon toga je izvršeno vrednovanje predloženih subjekata. Na taj način je izvršena primena teorije grubih skupova za rešavanje datih problema. Na kraju, nakon analize primenom teorije grubih skupova, izvršena je provera dobijenih rezultata metodama višekriterijumskog odlučivanja: VIKOR, AHP, ELECTRE, PROMETHEE i TOPSIS. Na osnovu dobijenih rezultata se došlo do zaključka da je u sva tri problema uspešno primenjena teorija grubih skupova. Poklapanje rezultata dobijenih metodom grubih skupova sa rezultatima dobijenim ostalim metoda višekriterijumskog odlučivanja se kreće od 100% poklapanja u slučaju izbora lokacije za flotacijsko jalovište, do 40% u slučaju izbora flotacijskog kolektora i flotacione mašine. Visoki koeficijenti korelacija demonstriraju visoku osetljivost metode grubih skupova u uslovima višekriterijumskog odlučivanja i determinišu njenu upotrebljivost u uslovima nedovoljnosti podataka.Mining, and mineral processing within it, is characterized by the complexity of the technological process due to the large number of influential parameters. Therefore it is necessary to be very cautious when making decisions in the field of mining. In order to provide a more efficient functioning of the processes, it is possible to apply methods that are used to streamline the decision-making process. One such method is the rough set theory. It is a relatively new mathematical theory that is suitable for understanding imprecise and incomplete data as well as to detect relationships between these data. The rough set theory has found application in various areas of industry, however, by reviewing literature it was concluded that the applicability of this theory in mining has not been sufficiently investigated. This was the starting point of a doctoral dissertation, examining the possibilities of its application in mining. In the experimental part of the dissertation was done testing the possibilities for application of rough set theory to solve three problems: choosing location for the flotation tailings landfill, choosing flotation collector and choosing flotation machine. The parameters that have the most influence during these three choices were determined, and they represented criteria for selection. The selected criteria were assigned with appropriate value, and subsequently the evaluation of the proposed subjects was carried out. In this way, rough set theory was applied for solving the given problems. Finally, results obtained by rough set theory, were verified by multi-criteria decision-making methods: VIKOR, AHP, ELECTRE, PROMETHEE and TOPSIS. Based on results it was concluded that rough set theory was successfully applied in all three problems. The superposition of the results obtained by the rough set theory with the results from other multi-criteria decision-making methods varies from 100% in the case of the choosing location for the flotation tailings landfill, to 40% in the case of choosing flotation collectors and flotation machines. High correlation coefficients demonstrate the high sensitivity of the rough set theory in terms of multi-criteria decision-making and define its usefulness in terms of insufficiency of data

Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition : a fuzzy rough set approach

Class imbalance occurs when data elements are unevenly distributed among classes, which poses a challenge for classifiers. The core focus of the research community has been on binary-class imbalance, although there is a recent trend toward the general case of multi-class imbalanced data. The IFROWANN method, a classifier based on fuzzy rough set theory, stands out for its performance in two-class imbalanced problems. In this paper, we consider its extension to multi-class data by combining it with one-versus-one decomposition. The latter transforms a multi-class problem into two-class sub-problems. Binary classifiers are applied to these sub-problems, after which their outcomes are aggregated into one prediction. We enhance the integration of IFROWANN in the decomposition scheme in two steps. Firstly, we propose an adaptive weight setting for the binary classifier, addressing the varying characteristics of the sub-problems. We call this modified classifier IFROWANN-WIR. Second, we develop a new dynamic aggregation method called WV–FROST that combines the predictions of the binary classifiers with the global class affinity before making a final decision. In a meticulous experimental study, we show that our complete proposal outperforms the state-of-the-art on a wide range of multi-class imbalanced datasets

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. First published online: 17 Sep 201

Assessing industrial ecosystem vulnerability in the coal mining area under economic fluctuations

In the context of the depth adjustment of the global economy and wild fluctuations in energy prices, the vulnerability issue of the coal mining industrial ecosystem (CMIES) has seriously affected the sustainable development of the regional economy. Comparisons of CMIES health status at a regional level are worthy of being conducted. This not only contributes to understanding a particular coal mining area's situation in regards to CMIES vulnerability, but also helps to discover a meaningful benchmark to learn the experiences in terms of action programmes formulation. In this study, based on the analysis of the vulnerability response mechanism of CMIES to economic fluctuations, an initial indicator system for vulnerability assessment of CMIES was constructed. Ultimately, 14 vulnerability-evaluating indicators and their weights were obtained using rough set attribute reduction. Based on a composite CMIES Vulnerability Index (CVI), the Rough Set-Technique for Order Preference by Similarity to Ideal Solution-Rank-sum Ratio (RS-TOPSIS-RSR) methodology is proposed to conduct the CMIES vulnerability assessment process from an overall perspective. Using this methodology, 33 coal mining areas in China are ranked as well as grouped into three specific groups based on the CVI score. The results demonstrate the feasibility of the proposed method as a valuable tool for decision making and performance evaluation with multiple alternatives and criteria