Soft Decision-making Research Articles

A New Kind of Generalized Pythagorean Fuzzy Soft Set and Its Application in Decision-Making

The aim of this paper is to introduce the concept of a generalized Pythagorean fuzzy soft set (GPFSS), which is a combination of the generalized fuzzy soft sets and Pythagorean fuzzy sets. Several of important operations of GPFSS including complement, restricted union, and extended intersection are discussed. The basic properties of GPFSS are presented. Further, an algorithm of GPFSSs is given to solve the fuzzy soft decision-making. Finally, a comparative analysis between the GPFSS approach and some existing approaches is provided to show their reliability over them.

Algorithm for possibility interval-valued neutrosophic soft decision-making based on distance measures settings

Soft set(SS) is one of the soft computing techniques that plays an important role in addressing the hiddenness and uncertainty associated with uncertain data. In other hand the idea of interval-valued neutrosophic soft sets (IVNSSs) is a new generalization of the neutrosophic soft sets to the neutrosophic sets when the authors combine the critical features of IVNS and soft sets (SSs) in one model. Accordingly, this model worked to provide decision-makers with more flexibility in the process of interpreting uncertain information. From a scientific point of view, the process of evaluating this high-performance IVNSS disappears. Therefore, in this paper, we initiated a new approach known as possibility interval-valued neutrosophic soft sets (PIVNSSs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on PIVNSSs along with their basic properties. Also, we investigate AND and OR operations between two PIVNSSs as well as several numerical examples to clarify the above fundamental operations. Finally, we have given distance measures (DM) between two PIVNSSs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

An efficient point-set registration algorithm with dual terms based on total least squares

Point set registration (PSR) is competitive with related techniques because it purposefully captures the overall structure between two point-set patterns. Typically, the point set registration problem can be divided into two sub-problems: (1) search point set correspondence (PSC); (2) estimate spatial transformation matrix (STM). Searching for the best PSC is a classical combinatorial explosion problem, and estimating the STM is a continuous space optimization problem. Also, two feature point sets detected by two low-quality images include point-position errors and often involve bilateral outliers composed of such feature points that cannot form a correspondence relationship. To address the above problems, we propose an efficient PSR algorithm with dual (symmetrical) terms based on the total least squares (DT-TLS), which can correct errors-in-variables and suppress multiple outliers. Meanwhile, the framework of soft decision-making is presented, and a TLS-based criterion is constructed to efficiently exploit the probability and global structures of two-point sets. Such TLS-based criterion with single row-orthonormal STM includes two interesting dual (symmetrical) terms that can be conveniently exploited to suppress bilateral outliers. The experimental results show that DT-TLS achieves better performance than the state-of-the-art algorithms in some multi-view computer vision tasks, indicating that our proposed algorithm is suitable for solving PSR problems.

Another View on Picture Fuzzy Soft Sets and Their Product Operations with Soft Decision-Making

Cuong [Picture Fuzzy Sets, Journal of Computer Science and Cybernetics 30 (4) (2014) 409–420] has introduced the concept of picture fuzzy soft sets (pfs-sets) relying on his definition and operations of picture fuzzy sets (pf -sets), in which there exist some inconsistencies. Yang et al. [Adjustable Soft Discernibility Matrix Based on Picture Fuzzy Soft Sets and Its Applications in Decision Making, Journal of Intelligent & Fuzzy Systems 29 (4) (2015) 1711–1722] have claimed that they have introduced the concept of pfs-sets with the inconsistencies in Cuong’s definition of pf -sets. Therefore, this study redefines the concept of pfs-sets to deal with the inconsistencies therein. Moreover, it investigates some of the properties of pfs-sets and their product operations and proposes a soft decision-making method via pfs-sets. Finally, pfs-sets, their product operations, and the proposed method are discussed for further research.

A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices fpfs -matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs -matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.

A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices

A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices

Generalisations of SDM Methods in fpfs-Matrices Space to Render Them Operable in ifpifs-Matrices Space and Their Application to Performance Ranking of the Noise-Removal Filters

Recently, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) has allowed to mathematically model some problems in which the parameters and alternatives exhibit intuitionistic fuzzy uncertainties. To this end, the present study aims to generalise 24 soft decision-making (SDM) methods operating in the fuzzy parameterized fuzzy soft matrices space with a single fpfs-matrix to the ifpifs-matrices spaces. Afterwards, we propose five test scenarios to analyse whether the SDM methods constructed by ifpifs-matrices consistently work. Moreover, we make performance comparisons of the generalised SDM methods successful in the five test scenarios by applying them to the performance-based value assignment (PVA) problem of the well-known noise-removal filters. Finally, we discuss the need for further research.

A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

Soft decision-making based on decision-theoretic rough set and Takagi-Sugeno fuzzy model with application to the autonomous fault diagnosis of satellite power system

The satellite power system is one of the core systems to ensure the normal on-orbit operation of satellite, and is also a representative of typical complex nonlinear systems. Autonomous prognostics and health management (A-PHM) is an inevitable trend in the future development of satellite, and autonomous fault diagnosis is a key part of A-PHM. Therefore, it is very necessary to carry out the research on autonomous fault diagnosis for satellite power system to improve the capability of satellite to perform its on-orbit tasks independently. Therefore, we put forward a feasible framework of soft decision-making to cope with this issue, mainly including attribute reduction, attribute weight assignment, rule extraction, and rule matching. Specifically, a neighborhood decision-theoretic rough set model (named DNDTRS) is first designed with the help of a data-driven loss function matrix for attribute reduction and weight assignment. Subsequently, the classification probability generated by DNDTRS is fed to a rule extraction model developed by the Takagi-Sugeno (T-S) fuzzy theory. Finally, a soft decision-making mechanism is proposed to execute the output after rule matching. In the experimental part, the proposed methodology is verified by the benchmark datasets and the fault data of satellite power system. The experimental results demonstrate the promised performance of our methodology.

Algorithms Based on COPRAS and Aggregation Operators with New Information Measures for Possibility Intuitionistic Fuzzy Soft Decision-Making

The objective of this paper is to present novel algorithms for solving the multiple attribute decision-making problems under the possibility intuitionistic fuzzy soft set (PIFSS) information. The prominent characteristics of the PIFSS are that it considers the membership and nonmembership degrees of each object during evaluation and their corresponding possibility degree. Keeping these features, this paper presents some new operation laws, score function, and comparison laws between the pairs of the PIFSSs. Further, we define COmplex PRoportional ASsessment (COPRAS) and weighted averaging and geometric aggregation operators to aggregate the PIFSS information into a single one. Later, we develop two algorithms based on COPRAS and aggregation operators to solve decision-making problems. In these approaches, the experts and the weights of the parameters are determined with the help of entropy and the distance measure to remove the ambiguity in the information. Finally, a numerical example is given to demonstrate the presented approaches.

CONFIGURATIONS OF SEVERAL SOFT DECISION-MAKING METHODS TO OPERATE IN FUZZY PARAMETERIZED FUZZY SOFT MATRICES SPACE

The concept of fuzzy parameterized fuzzy soft matrices (fpfs-matrices), which allows for processing fuzzy parameters and fuzzy subsets of the alternatives by using computers, is a novel and efficient mathematical tool to cope with uncertainties. Contrary to the methods constructed by fpfs-matrices, the known soft decision-making (SDM) methods based on soft sets and fuzzy sets cannot model problems whose parameters and alternatives are fuzzy. Therefore, such methods have been configured to operate in the fpfs-matrices space. In this paper, we configure two SDM methods constructed by soft sets, six SDM methods constructed by fuzzy soft sets, two SDM methods constructed by soft matrices, and four SDM methods constructed by fuzzy soft matrices. We then apply the configured methods using one fpfs-matrix as input data to a performance-based value assignment problem. Finally, we discuss the need for further research.

Classification of The Monolithic Columns Produced in Troad and Mysia Region Ancient Granite Quarries in Northwestern Anatolia via Soft Decision-Making

Ay and Tolun [An Archaeometric Approach on the Distribution of Troadic Granite Columns in the Western Anatolian Coasts. Journal of Archaeology & Art, 156, 2017, 119-130 (In Turkish)] have analysed the distribution of the monolithic columns produced in the ancient granite quarries, located in Troad Region and Mysia Region in Northwestern Anatolia, by archaeometric analyses. Moreover, we have achieved some results by interpreting the prominent data obtained therein. In this study, we propose a novel soft decision-making method, i.e. Monolithic Columns Classification Method (MCCM), constructed via fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and Prevalence Effect Method (PEM). MCCM provides an outcome by interpreting all the results of the analyses mentioned above. We then apply the method to the problem of monolithic columns classification. Finally, we discuss the need for further research.

A Configuration of Some Soft Decision-Making Algorithms via fpfs-matrices

Since many problems have a large amount of data or uncertainty, the computer mathematics has become compulsory. To deal with such kinds of these problems, the concept of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) has been defined by Enginoğlu. In this paper, we first give some of its basic definitions. We then configure some decision-making algorithms constructed by soft sets, fuzzy soft sets, fuzzy parameterized soft sets, fpfs-sets, and their matrix representations. We finally discuss later works.

Energy efficient cooperative spectrum sensing in wireless multi-antenna sensor network

This paper will address sensor selection problem for spectrum sensing in a cognitive radio network. The sensor's limited energy is an important issue which has attracted more attention in recent years. An energy efficient cooperative spectrum sensing will hereby be proposed when multi-antenna sensors are used. Two decision-making techniques are utilized for the combination of antennas' signals in each sensor: hard and soft decision-making. OR rule is used for hard decision-making technique while selection combining, equal gain combining and maximum ratio combining (MRC) are used for the soft one. In each combination scheme, the sensor selection is a problem by means of which both the energy consumption is minimized and the detection performance gets satisfied. The problem is solved based on the standard convex optimization method. Simulation results show the achievement of a significant energy saving compared to the networks using single-antenna sensors specifically in low signal to noise ratio state. Among all methods, MRC combining enjoys the least energy consumption, as well; it satisfies the desired detection performance.

Fuzzy match index for scale‐invariant feature transform (SIFT) features with application to face recognition with weak supervision

A fuzzy match index for scale-invariant feature transform (SIFT) features is proposed in this study that cumulatively involves all the test SIFT keypoints in the decision-making process. The new fuzzy SIFT classifier is adapted successfully for robust face recognition from complex backgrounds without using any face cropping tools and using only a single training template. The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision-making as compared with the background features. The highlights of the authors’ work are: (i) The development of a novel highly efficient fuzzy SIFT descriptor matching tool; (ii) incorporation of feature entropy weights to highlight the contribution of facial features; (iii) application to robust face recognition from uncropped images having diverse backgrounds with a single template for each subject. The authors thus allow for weak supervision of the face recognition experiment and obtain high accuracy for 20 subjects of the CALTECH-256 face database, 133 subjects of the labelled faces for the wild dataset and 994 subjects of the FERET database, with state-of-the-art comparisons indicating the supremacy of the authors’ approach.

Soft Decision-Making SVM Using in Fault Detection and Diagnosis

Soft Decision-Making SVM Using in Fault Detection and Diagnosis

Single-Channel Noise Reduction for Multiple Background Noises Using Perceptual Wavelet Packet Transform and Fuzzy Logic

Background noises interfere with communication devices such as mobile telephone, digital hearing aid, etc. Therefore noise reduction (NR) part for limiting the effect of these noises is important. The paper proposes a noise reduction method based on the soft decision-making by the fuzzy inference system (FIS). The different characteristics of noises frequently occurring are used for creating the fuzzy decision rule base of the FIS. The FIS have two input parameters: the average energy and the difference of the average energy. The analysis of the FIS is done in the domain of the perceptual wavelet packet transform (PWPT) that is the human’s psychoacoustic model. The output of the FIS is used to modify the PWPT coefficients in such a way that it is more likely that the noise components are reduced while the speech signal is enhanced. The enhanced speech signal is the result of the inverse perceptual wavelet packet transform (IPWPT) of the modified coefficients. The experiment results show that the proposed method gives lower distortion than do the conventional methods especially when the input signal-to-noise ratio (SNR) is low; e.g. at SRN at 0dB the proposed method improves the output SNR level up to 4.18dB.

Soft computing approach to feature extraction

Based on both wavelet theory and fuzzy theory, a soft computing system (SCS) is proposed for feature extraction of signals. The proposed SCS approach possesses the advantages of soft decision-making on wavelet coefficients for feature extraction, adaptive selectivity of mapping factors to coarse-to-fine resolution, and compact form of feature representation with the SCS feature-extractor. Fuzzy sets are used to provide a robust representation for signal information, and wavelet transform is used to decompose a signal into detail and approximation signals. At a given resolution, the detail and approximation signals are inputted to the proposed SCS to extract signal features at that resolution level. The sensitivity in feature extraction of the proposed approach can be adapted by tuning the fuzzy sets for the detail and approximation signals. At different resolutions, the signal can be examined and suitable features can be extracted. Examples of both one-dimensional signals and two-dimensional fingerprint images are used to illustrate the proposed soft computing approach for feature extraction and pattern recognition. The results show that the extracted features are sensitive enough to distinguish the similar signal from the different ones and are robust enough to tolerate noise corruption.