Narrow Results

In order to give attention to the smooth distortion of two image mosaic evaluation indices concurrently, an image multiscale mosaic method based on fuzzy integral is proposed in this paper. Aiming at problems resulting from evaluating mosaic effect of single-factor evaluation index, the integrative evaluation method of the mosaic image based on synthesizing two independent single-factor indices is put forward by using a fuzzy integral which is largely subject to human feeling. According to the optimal image mosaic rules, an optimal weighted function is obtained. With the help of the optimal weighted function, wavelet coefficients are fused at different resolution levels. As a result, a smooth and seamless mosaic image is reconstructed. Finally, experiments are performed on the method presented in this paper.

Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).

The natural properties of the aggregation operators and the most elementary ones are the idempotence, the monotonicity and the continuity from below. We assume only these properties for the aggregation operators with infinitely many inputs, defined by functionals on the family of measurable functions. A family of fuzzy measures is associated with each aggregation operator. The properties of horizontal or vertical pseudo-additivity are recognizable by means of this family of fuzzy measures.

Preference Ranking Organization METHods for Enrichment Evaluations (PROMETHEE), based on outranking relation theory, are used extensively in Multi-Criteria Decision Aid (MCDA). In PROMETHEE, an overall preference index based on weighted average aggregation represents the intensity of preference for one pattern over another pattern and can be measured by a given preference function. Unfortunately, as the criteria making up the patterns are not always independent, the assumption of additivity among single-criterion preference indices may not be reasonable. This paper develops a novel PROMETHEE-based perceptron using nonadditive preference indices for ordinal sorting problems. The applicability of the proposed non-additive PROMETHEE-based single-layer perceptron (SLP) to bankruptcy prediction is examined by using a sample of 53 publicly traded, Taiwanese firms that encountered financial failure between 2000 and 2008. The proposed model performs well compared to PROMETHEE with additive preference indices and other additive PROMETHEE-based classification approaches.

Extreme learning machine (ELM) is an efficient and practical learning algorithm used for training single hidden layer feed-forward neural networks (SLFNs). ELM can provide good generalization performance at extremely fast learning speed. However, ELM suffers from instability and over-fitting, especially on relatively large datasets. Based on probabilistic SLFNs, an approach of fusion of extreme learning machine (F-ELM) with fuzzy integral is proposed in this paper. The proposed algorithm consists of three stages. Firstly, the bootstrap technique is employed to generate several subsets of original dataset. Secondly, probabilistic SLFNs are trained with ELM algorithm on each subset. Finally, the trained probabilistic SLFNs are fused with fuzzy integral. The experimental results show that the proposed approach can alleviate to some extent the problems mentioned above, and can increase the prediction accuracy.

The techniques of fuzzy measure and fuzzy integral have been successfully applied in various real-world applications. The determination of fuzzy measures is the most difficult part in problem solving. Signed efficiency measure, which is a special kind of fuzzy measure with the best representation ability but the highest complexity, is even harder to determine. Some methodologies have been developed for solving this problem such as artificial neural networks (ANNs) and genetic algorithms (GAs). However, none of the existing methods can outperform the others with unique advantages. Thus, there is a strong need to develop a new technique for learning distinct signed efficiency measures from data. Extreme learning machine (ELM) is a new learning paradigm for training single hidden layer feed-forward networks (SLFNs) with randomly chosen input weights and analytically determined output weights. In this paper, we propose an ELM based algorithm for signed efficiency measure determination. Experimental comparisons demonstrate the effectiveness of the proposed method in both time and accuracy.

In this paper we consider Steffensen's integral inequality for the Sugeno integral

This paper propose a Berwald type inequality and a Favard type inequality for Sugeno integrals. That is, we first show that

A pair of new integrals, a PFB-integral and a PFA-integral, are introduced. Given a measurable space, these integrals are defined for nonnegative functions with respect to monotone set functions, and they are generalizations of both the Lebesgue integral and the fuzzy integral. Some primary properties of the PFB-integral and PFA-integral are discussed and an inequality between them is established in this paper.

First, a generalized fuzzy integral which encompasses Sugeno integral, Choquet integral and opposite-Sugeno integral is proposed. A more restricted fuzzy integral can be expressed by using only one parameter of Frank's t-norm. It also encompasses the three integrals. Next, the performance when the integral is adopted as a subjective evaluation model is theoretically investigated. Finally, an example of subjective evaluation test is shown and is discussed.

This paper formulates "partition type fuzzy integral" which is a fuzzy integral of functions on a partition of the set of all attributes. The identity of it is the set of intermediate fuzzy integrals between the linear form and the usual multilinear fuzzy integrals. In this fuzzy integral, any combination of attributes in the same macro-attribute yields no interactions, but any set of attributes, each member of which belongs to a distinct macro-attribute, causes an interaction. Therefore, we can extract only the essential interactions from the evaluation process by discovering the best expressive partition. An application to a concrete evaluation problem shows the effectiveness of this model.

In this paper, a hybrid feature extraction technique using 2D principal component analysis (2DPCA) and discrete orthogonal Krawtchouk moment (KM) are used to extract the global and local features from the face. Ensemble of RBF classifiers are used to classify the image. Decision-level fusion is done using fuzzy integral to generate more accurate classification than each of the constituent classifiers. The proposed system is evaluated using ORL and YALE databases. Experimental results show that the combination of global and local features promotes the system performance. The fusion of multiple RBFs using fuzzy integral performed better as compared to conventional aggregation rules.

This paper proposes a decision model with a dashboard for systematically selecting and improving the performance of supplier in green supply chain management. The model combines both the decision-making trial and evaluation laboratory-based analytical network process (DANP) with a fuzzy integral method. The DANP method is used to construct the network system and derives the weights. The fuzzy integral method is applied to avoid inconsistent assumptions between the interdependence relation and linear aggregation for obtaining the aspiration-gaps of green suppliers. Last, the visualized dashboard allows for a clear representation of the interdependent-network structure of the attributes to assist companies in the selection of suitable suppliers, as well as assisting the suppliers in improving their performance with higher levels of service. A Taiwanese Electronics Company is demonstrated as an example. The results demonstrate that the dashboard can enable managers to make better decisions from a systematic perspective.

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

Recently, wavelet transform and fusion technology have been used in face recognition. In this paper, we propose a new face recognition method based on probability-based fuzzy integral and wavelet transform. Firstly, an image is decomposed with wavelet transform (WT) to three levels. Secondly, the Fisherface method is applied to three low-frequency subimages respectively. Then, the individual classifiers are fused with fuzzy integral based on probability measures. Experimental results show that the new method outperforms individual classifiers. Furthermore, we compare the fuzzy integrals based on probability measure with those based on the commonly used g_λ - fuzzy measures with λ ≥ -1 and λ ≠ 0, and find that the former produces consistently better results than the latter.

The fuzzy classification plays an important role to predict defect of software modules. In this paper, the fuzzy measure (FM) is used to improve the predict accuracy and capability by acquiring all possible interactions among metrics and apply Choquet integral (CI) for classifying in n dimensional space and automatic searching the least misclassification rate based on distance. To implement the model, we also need to determine the unknown parameters, and which is implemented using genetic algorithm (GA) on the training data. The proposed model is tested on the four NASA software projects. The results indicate that the predict performance of proposed model is better than other predict models.