Ahmad Nazari Mohd Rose

Soft Set Theoretic Approach for Dimensionality Reduction

A reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of a given information system. The existing reduct approaches under soft set theory are still based on Boolean-valued information system. However, in the real applications, the data usually contain non-Boolean valued. In this paper, an alternative approach for attribute reduction in multi-valued information system under soft set theory is presented. Based on the notion of multi-soft sets and AND operation, attribute reduction can be defined. It is shown that the reducts obtained are equivalent with Pawlak’s rough reduction.

A framework of decision making based on maximal supported sets

In this paper, we present an alternative technique of decision making through parameterization reduction by determining maximal supported sets from a Boolean-valued information system based soft set theory Based on such reduction, the maximal support will be calculated to determine the optimal choice It is shown that the technique is identical to normal parameter reduction from previous research on soft set for decision making While maximal support reduction is in fact has also provided consistency choices in decision making.

Soft set theoretic approach for discovering attributes dependency in information systems

This paper presents the applicability of soft set theory for discovering attribute dependency in multi-valued information systems The proposed approach is based on the notion of multi-soft sets An inclusion of value sets in soft set theory is used to discover degree of attributes dependency The results obtained are equivalent to the rough attributes dependency.

Solving Incomplete Datasets in Soft Set Using Parity Bits of Supported Sets

The theory of soft set proposed by Molodtsov [2]in 1999 is a new method for handling uncertain data and can be redefined as a Boolean-valued information system. The soft set theory has been applied to data analysis and decision support systems based on large datasets. Using retrieved datasets, we have calculated the supported values and then determine the even parity bits. Using the parity bit, the problem of missing values from the retrieved datasets can be solved.

Hybrid reduction in soft set decision making

In this paper, we present an extended technique of decision making by implementing column reduction with reduction based on calculated maximal support objects. Using a Boolean valued information system, certain rows or objects can be defined as ultimate maximum support object, ultimate minimum support object and zero significance parameter. One can then reduce a table by eliminating the defined row or objects in what has been defined as hybrid reduction. As part of our paper, we have managed to show that our proposed model of hybrid reduction yielded a better data size reduction whilst still maintaining consistent results.

Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values

Abstract The theory of soft set proposed by Molodtsovin 1999[1]is a new method for handling uncertain data and can be defined as a Boolean-valued information system. Ithas been applied to data analysis and decision support systems based on large datasets. In this paper, it is shown that calculated support value can be used to determine missing attribute value of an object. However, in cases when more than one value is missing, the aggregate values and calculated support values will be used in determining the missing values. By successfully recovering missing attribute values, the integrity of a dataset can still been maintained.

Dominance-based soft set approach in decision-making analysis

Multi-criteria decision analysis, sometimes called multi-criteria decision making, is a discipline aimed at supporting decision makers faced with making numerous and sometimes conflicting evaluations. Multi-criteria decision analysis aims at highlighting these conflicts and providing a compromised solution in a transparent process. This paper introduces the application of soft-dominance relation based on soft set theory in the field of multi-criteria decision analysis. This relation is an extension of the soft set theory which deals with typical inconsistencies during the consideration of criteria and in preference-ordered decision classes. The paper also utilized soft-dominance relations based on soft set theory in obtaining the decision rules in dealing with problems in a multi-valued information system.

Soft set approach for selecting decision attribute in data clustering

This paper presents the applicability of soft set theory for discovering a decision attribute in information systems. It is based on the notion of a mapping inclusion in soft set theory. The proposed technique is implemented with example test case and one UCI benchmark data; US Census 1990 dataset. The results from test case show that the selected decision attribute is equivalent to that under rough set theory.

Multiple Criteria Preference Relation by Dominance Relations in Soft Set Theory

This paper presents the applicability of soft set theory for discovering the preference relation in multi-valued information systems. The proposed approach is based on the notion of multi-soft sets. An inclusion of objects into value set of decision class in soft set theory is used to discover the relation between objects based on preference relation. Results from the experiment shows that dominance relation based on soft theory for preference relation is able to produce a finer object classification by eliminating inconsistencies during classification process as opposed to the expert judgement classification.

A Fuzzy TOPSIS with Z-Numbers Approach for Evaluation on Accident at the Construction Site

The construction industry has been identified as one of the most risky industries where involves fatalities accidents. Identifying the causes that lead to the accidents implicates a lot of uncertain and imprecise cases. Z-numbers involve more uncertainties than Fuzzy Sets (FSs). They provide us with additional degree of freedom to represent the uncertainty and fuzziness of the real situations. In this paper, we introduce a Fuzzy TOPSIS (FTOPSIS) with Z-numbers to handle uncertainty in the construction problems. Five criteria and six alternatives are used to evaluate the causes of workers’ accident at the construction sites. Data in form of linguistic variables were collected from three authorised personnel of three agencies. From the analysis, it shows that the FTOPSIS with Z-numbers provides us with an another useful way to handle Fuzzy Multi-Criteria Decision Making (FMCDM) problems in a more intelligent and flexible manner due to the fact that it uses Z-numbers with FTOPSIS.