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Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance. [ABSTRACT FROM AUTHOR]

In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features. [ABSTRACT FROM AUTHOR]

Attribute reduction is considered as an important processing step for pattern recognition, machine learning and data mining. In this paper, we combine soft set and rough set to use them in applications. We generalize rough set model and introduce a soft metric rough set model to deal with the problem of heterogeneous numerical feature subset selection. We construct a soft metric on the family of knowledge structures based on the soft distance between attributes. The proposed model will degrade to the classical one if we specify a zero soft real number. We also provide a systematic study of attribute reduction of rough sets based on soft metric. Based on the constructed metric, we define co-information systems and consistent co-decision systems, and we provide a new method of attribute reductions of each system. Furthermore, we present a judgement theorem and discernibility matrix associated with attribute of each type of system. As an application, we present a case study from Zoo data set to verify our theoretical results. [ABSTRACT FROM AUTHOR]

In the context of optimal subset selection, hybrid feature selection approaches play a significant role that has been the topic of a substantial number of studies because of the growing need for data mining applications. In feature subset selection (FSS) problem; there are two significant shortcomings that need to be addressed: At first, finding a suitable filter method that can be reasonably fast and energetically computed for large volume of data, and second, an efficient wrapper strategy that can discriminate the samples over the entire search space in a considerable amount of time. After a study of the shortcomings of individual feature selection methods (filter or wrapper), this paper investigated a new hybrid feature selection approach with conjunction of filter and wrapper method that can take benefit of both ways for a classification problem. The proposed hybrid uses the filter method as conditional mutual information maximization and wrapper method as genetic algorithm to enhance the overall classification performance and speed up the search process to identify the essential features. The proposed method is known as FWFSS. To get rid of meaningless features and determine the biomarkers, wrapper method as genetic algorithm uses the naïve Bayes (NB) classifier as a fitness function. The proposed method is verified on the University of California, Irvine (UCI) repository, and microarray datasets. From experimental study, it is observed that our approach outperforms convenient methods regarding classification accuracy, the number of optimal features reported in the literature. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to introduce and examine the concepts of deferred statistical convergence of order β and strong r-deferred Cesáro summability of order β for sequences of fuzzy numbers and give some relations between deferred statistical convergence sequences of order β and strongly r-deferred Cesáro summable sequences of order β, where β ∈ (0, 1] and p ∈ ℝ +. Mathematics Subject Classification: 40A05, 40C05, 46A45, 03E72. [ABSTRACT FROM AUTHOR]

Recently, available data has increased explosively in both number of samples and dimensionality. The huge number of high dimensional data generates the presence of noisy, redundant and irrelevant dimensions. Such dimensions can increase the time and computational cost in the learning process and even degenerate the performance of learning tasks. One of the ways to reduce dimensionality is by Feature Selection (FS). The aim of this paper is study the feature selection based on expert knowledge and traditional methods (filter, wrapper and embedded) and analyze their performance in classification tasks. Three datasets related to cancer domain in humans were used for feature selection: Breast Cancer (BC), Primary Tumor (PT) and Central Nervous System (CNS). C4.5, K-Nearest Neighbors, Support Vector Machine and Multi Layer Perceptron were trained with the best subset of features for each cancer dataset. The subset of features selected by the wrapper method presents the best average accuracy in the datasets BC and PT, while the subset of features selected by the embedded method reaches the highest average accuracy in the CNS dataset. [ABSTRACT FROM AUTHOR]

Feature selection is one of the key problems in machine learning and data mining. It involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. It can reduce the dimensionality of original data, speed up the learning process and build comprehensible learning models with good generalization performance. Nowadays, ensemble idea has been used to improve the performance of feature selection by integrating multiple base feature selection models into an ensemble one. In this paper, in order to improve the efficiency of feature selection in dealing with large scale, high dimension and imbalanced problems, a Min-Max Ensemble Feature Selection (M2-EFS) is proposed, which is based on balanced data partition and min-max ensemble strategy. The experimental results demonstrate that the M2-EFS can obtain higher performance than other classical ensemble methods in most cases, especially for large scale, high dimension and imbalanced data. [ABSTRACT FROM AUTHOR]

In this paper, the degree to which an L-subset of a vector space is an L-convex set is introduced and their characterizations are given. Besides, we obtain an L-fuzzy convexity induced by L-convex degrees, and analyze their L-fuzzy convexity preserving mappings and L-fuzzy convex-to-convex mappings. [ABSTRACT FROM AUTHOR]

The concept of A-subset is introduced in lattice implication algebras. Firstly, the properties of the A-subset are discussed when A is a general set of lattice implication algebras. Next, the properties of A-subset are investigated when the A is an LI-ideal of lattice implication algebras. We obtain some properties of A-subset and prove that B(A)(the A-subset of B) is an LI-ideal in lattice implication algebras. Finally, the properties of A-subset are investigated in lattice implication product algebras. We prove that the A-subset of ⨅ni=1 Bi is the product of A-subset of Bi(i = 1, 2, √, n) respectively ((⨅ni=1 Bi) (⨅ni=1 Ai)(⨅ni=1 Bi (Ai)). [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of interior Γ-hyperideals of ordered Γ-semihypergroups, which is a generalization of the concept of interior Γ-hyperideals of Γ-semihypergroups to ordered Γ-semihypergroup theory, and investigate its related properties. Furthermore, we define and discuss the fuzzy interior Γ-hyperideals of ordered Γ-semihypergroups. In particular, we give the characterization of fuzzy interior Γ-hyperideal generated by a fuzzy subset in an ordered Γ-semihypergroup. In addition, we consider the concepts of (λ,μ)-fuzzy Γ-hyperideals and (λ,μ)-fuzzy interior Γ-hyperideals in ordered Γ-semihypergroups. Finally, we introduce the notion of (λ,μ)-fuzzy simple ordered Γ-semihypergroups and prove that an ordered Γ-semihypergroup is simple if and only if it is (λ,μ)-fuzzy simple. Especially, characterization of simple ordered -semihypergroups in terms of (μ)-fuzzy interior-hyperideals is given. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notions of pairwise soft sub kernel, and pairwise ⋁-soft sets in a soft bitopological space (X, η1, η2,E). Also, we study the fundamental properties of pairwise ⋁-soft sets and we investigate the associated soft topology ηp⋁. Moreover, we introduce the notions of generalized pairwise ⋀(⋁)-soft sets and we investigate their basic properties. Also, we define a soft closure operator on the family of all generalized pairwise ⋀-soft sets and generate, in usual manner, an Alexandroff soft topology ηgp⋀ on X which is finer than ηp⋁. Furthermore, we ⋁prove that (X, ηgp⋀,E) is always a soft T1/2 space. In addition, we introduce characterizations of pairwise soft T*1/2 by using generalized pairwise ⋀-soft sets and we show that the concepts of generalized pairwise ⋀-soft set and generalized pairwise closed soft set are independent. [ABSTRACT FROM AUTHOR]

A fuzzy soft set is a mapping from parameter set to the fuzzy subsets of universe. Fuzzy soft set theory provides a parameterized point of view for uncertainty modeling and soft computing model. In this research article, we apply the concept of fuzzy soft sets to graphs. We present the concept of fuzzy soft graphs, various methods of their construction, and investigate some of their related properties. We discuss certain types of irregular fuzzy soft graphs. We also describe applications of fuzzy soft graphs in social network and road network. [ABSTRACT FROM AUTHOR]

Notion of singular fuzzy ideals of commutative rings is introduced in this paper. These singular fuzzy ideals are defined via essential fuzzy ideals. Various characteristic features of such ideals to establish relationship between fuzzy non singularity and fuzzy semiprime character of commutative rings are presented. [ABSTRACT FROM AUTHOR]

In this paper by considering of congruence relations induced by fuzzy ideals, we study rough sets in BCK-algebras. To this purpose we clarify a lower and upper approximations for any subset of Χ in this algebras. Finally, lower and upper rough ideals with respect to fuzzy ideal μ of Χ are discussed. [ABSTRACT FROM AUTHOR]

In this paper, first we investigate some properties of L-ordered groups such as L-subgroup generated by an arbitrary subset and product ofL-ordered groups. Then we introduce the notions of totallyL-ordered group and strong totallyL-ordered group, where L is a frame and some related results are obtained. We show that the set of all isomorphisms on a totally Lsubgroup, forms an L-ordered group. Moreover, if it is a strong totally L-ordered group, then the set of all isomorphisms on it is an L-lattice ordered group. [ABSTRACT FROM AUTHOR]

Processing a huge amount of information takes extensive execution time and computational sources most of the time with low classification accuracy. As a result, it is needed to employ a phase of pre-analysis processing, which can influence the performance of execution time and computational sources and classification accuracy. One of the most important phases of pre-processing is Feature selection, which can improve the classification accuracy of steganalysis. The experiments are accomplished by using a large and important data set of 686 features vectores named SPAM. One of the promising application domains for such a feature selection process is steganalysis. In this paper, we propose a new metaheuristic approach for image steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on an improved artificial bee colony. Within the ABC structure the k-Nearest Neighbor (kNN) method is employed for fitness evaluation. ABC and kNN have been adjusted together to make an operative dimension reduction method Experimental results demonstrate the effectiveness and accuracy of the proposed technique compared to recent ABC-based feature selection methods and other existing techniques. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two different stabilizers of X with respect to Y for subsets X and Y of an MV-algebra A. We show that stabilizers are ideals and we investigate the relation between stabilizers and some other ideals in MV-algebras. We also show that if I and J are ideals in an MV-algebra A, then the stabilizer of I with respect to J is the relative pseudo-complement of I with respect to J in the lattice of ideals of A. [ABSTRACT FROM AUTHOR]

The aim of this paper is introduce the concepts of FP-fuzzy soft h-ideals and FP-equivalent fuzzy soft h-ideals of hemirings. Some characterizations and properties of them are given. In particular, some good examples are explored. Finally, we investigate aggregate fuzzy h-ideals of hemirings. [ABSTRACT FROM AUTHOR]