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We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain–computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimodal clouds. The images are then compared either by using the Mahalanobis distance or by the support vector data description (SVDD), respectively.

The paper investigates some possibilities of combining the image clouds based on the idea that responses of several cloud descriptions may convey a pattern, specific for semantically similar images. A ranking of image dissimilarities is used as a comparison for two image databases targeting image classification and retrieval problems. We show that combining of the SVDD descriptions improves the retrieval performance with respect to ranking, on the contrary to the Mahalanobis case. Surprisingly, it turns out that the ranking of the Mahalanobis distances works well also for inhomogeneous images.

In order to improve the performance in pattern classification, we utilize multiple classifiers and combine their individual decisions to make a final decision. In this paper, we present the combination using Bayesian method and compare minimum errors. This method requires the posteriori probabilities from all classifiers, which may be difficult to calculate in real world because tremendous amounts of training samples are needed. Alternatively, a confusion matrix is developed for approximation. We also use different combining rules for comparisons and apply them to handwritten digit recognition.

Combining outputs of a pool of individual classifiers appropriately, as a hot research topic of pattern classification, can generate statistically significant increase in classification performances. During the last decades, several fusion algorithms were presented, but few of those focus on two-class classification which possesses wide application area such as sentiment classification, cancer differentiation and so on. Thus the main purpose of this paper is to develop a highly effective fusion algorithm, i.e. intuitionistic fuzzy reasoning fusion algorithm, to increase the performance of a multiple two-class classifiers system. The outputs of component classifiers are represented by a set of intuitionistic fuzzy values at first and the fusion process is interpreted as aggregation of intuitionistic fuzzy information. The proposed algorithm includes three versions using the intuitionistic fuzzy arithmetic average operator, intuitionistic fuzzy weighted average operator and induced intuitionistic fuzzy ordered weighted average aggregation operator, respectively. The proposed fusion algorithm can combine both evidences of the hypothesis that a test pattern belongs to a class and evidences of the hypothesis that the test pattern does not belong to the class at the same time. We compare the versions of our algorithm with four other fusion techniques on five open-access datasets. The proposed algorithm exhibits good predictive abilities, compared to the best individual classifiers and other comparable fusion techniques. Further, the experiments show some interesting results about measuring and weighting component classifiers.

Every year, agriculture experience significant economic loss due to wild geese, rooks and other flocks of birds. A wide range of devices to detect and deter animals causing conflict is used to prevent this, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviors could form a critical component of a system capable of altering the disruptive stimulus to avoid habituation. This paper presents an audio-visual-based approach for recognition of goose flocking behavior. The vocal communication and movement of the flock is used for the audio-visual recognition, which is accomplished through classifier fusion of an acoustic and a video-based classifier. Acoustic behavior recognition is based on generalized perceptual features and support vector machines, and visual behavior recognition is based on optical flow estimation and a Bayesian Rule-Based scheme. Classifier fusion is implemented using the product rule on the soft-outputs from both classifiers. The algorithm has been used to recognize goose flocking behaviors (landing, foraging and flushing) and have improved the performance compared to using audio- or video-based classifiers alone. The improvement of using classifier fusion is most evident in the case of flushing and landing behavior recognition, where it was possible to combine the advantages of both the audio- and video-based classifier.

Emotions indicate the feelings of the individual which are linked with personal experiences, moods, and affective states. Detection of emotion can be helpful in many fields like maintaining a patient’s psychological well-being, surveillance, driver monitoring, etc. In this paper, an effective machine learning approach has been put forth for emotion detection where an ensemble of three out of five best-performing classifiers has been formed to enhance the classification accuracy. Two deep learning models (AlexNet and ResNet) have been optimally combined with $k$-nearest neighbor (KNN). The optimal weights for ensemble weighted averaging of classifiers have been computed with aid of particle swarm optimization (PSO) and genetic algorithm (GA) optimization. The developed framework has been tested on two publicly available datasets. An overall accuracy of above 95% has been achieved on the testing set for both datasets. The best performance was obtained by training the classifiers with segmented images and combining them by using the weights obtained through PSO. The results depicted the efficiency of the optimized ensemble machine learning approach for all performance measures used in this study in comparison to the performance of individual classifiers and majority voting fusion.

The time or space complexity may considerably increase for a single classifier if all features are taken into account. Thus, it is reasonable to train a single classifier by partial features. Then, a set of multiple classifiers can be generated, and an aggregation of outputs from different classifiers is subsequently performed. The aim of this paper is to propose a classification system with a heuristic fusion scheme in which multiple fuzzy association rule-based classifiers with partial features are combined, and show the feasibility and effectiveness of fusing multiple classifiers through the Sugeno integral extended by ordered weighted averaging operators. In comparison with the Sugeno integral by computer simulations on the iris data and the appendicitis data show that the overall classification accuracy rate could be improved by the Sugeno integral with ordered weighted averaging operators. The experimental results further demonstrate that the proposed method performs well in comparison with other fuzzy or non-fuzzy classification methods.

Dialog act (DA) classification is useful to understand the intentions of a human speaker. An effective classification of DA can be exploited for realistic implementation of expert systems. In this work, we investigate DA classification using both acoustic and discourse information for HCRC MapTask data. We extract several different acoustic features and exploit these features using a Hidden Markov Model (HMM) network to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features. To classify discourse information, we exploit two classifiers such as a HMM and Support Vector Machine (SVM). We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify 12 different DAs in MapTask data. We obtain 65.2% and 55.4% DA classification rates using acoustic and discourse information, respectively. Furthermore, we obtain combined accuracy of 68.6% for DA classification using both acoustic and discourse information. These accuracy rates of DA classification are either comparable or better than previously reported results for the same data set. For average precision and recall, we obtain accuracy rates of 74.89% and 69.83%, respectively. Therefore, we obtain much better precision and recall rates for most of the classified DAs when compared to existing works on the same HCRC MapTask data set.

Combining output classifiers has been one of the strategies used to outperform classifications rates on general purpose classification problems. In this paper we propose two linear combination rules that minimize the classification error under some constraints in two-class classifiers.

This paper addresses the problem of pose invariant generic object recognition by proposing a novel framework which fuses information obtained from 3D appearance and 2D shape cues. The 3D appearance model of the object is captured using linear subspace analysis techniques (2D PCA and ICA) and is used to reduce the search space to a few rank-ordered candidates. For matching based on 2D shape features, we propose the use of distance transform based correlation. We have explored various measures for fusion of 3D appearance and 2D shape cues. Fusion of two linear subspace classifiers, 2D PCA and ICA, gives the best set of rank ordered samples, and then a combined decision of distance transform based shape matching and subspace classifiers selects the object class and pose for recognition. Experiments were conducted on COIL-100 database which contains a wide variety of objects from different classes with complex appearance and shape characteristics. The proposed technique is shown to outperform the other existing techniques in literature. Experimentation shows that the proposed method is more robust to noise.