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A new multiple classifier system (MCS) is proposed based on CTSP (classification based on Testing Sample Pairs), which is a kind of applicable and efficient classification method. However, the original output form of the CTSP is only crisp class labels. To make use of the information provided by the classifier, in this paper, the output of CTSP is modeled using the membership function. Then, the fuzzy-cautious...
Numbers of samples in different classes are in nature imbalanced in many machine learning problems. Single classifier-based methods are subject to high variance. Therefore, ensemble-based methods are more suitable for dealing with imbalanced pattern classification problems. In this work, we propose a boosting-based method: BSMBoost which creates an ensemble of classifiers using samples selected by...
Traditional domain adaptation methods attempted to learn the shared representation for distribution matching between source domain and target domain where the individual information in both domains was not characterized. Such a solution suffers from the mixing problem of individual information with the shared features which considerably constrains the performance for domain adaptation. To relax this...
The Diversified Sensitivity-based Undersampling (DSUS) is an undersampling method to solve the imbalance pattern classification problems which overcomes the drawbacks of ignoring the distribution information of the training dataset in random-based undersampling methods. The DSUS trains multiple neural networks during the undersampling process. However, only the final one is used. In this work, we...
Information fusion aims to exploit truthful knowledge from various sources in a reliable and accurate way. Fusion of information can be conducted at three abstraction levels including feature level, score level and decision level. The feature fusion approaches have the advantages of preserving effective discriminative structure underlying various features. In this paper, we propose an effective feature...
In the complex pattern classification problem, the fusion of multiple classification results produced by different attributes is able to efficiently improve the accuracy. Evidence theory is good at representing and combining the uncertain information, and it is employed here. Each attribute (set) can be considered as one source of evidence (information). In some applications, the observation of target...
Assessment of aging civil infrastructure should be done periodically to getting information about the structural condition. In context to it, classification, detection, and localization of cracks within these concrete structures is of paramount importance. The most commonly used procedure, i.e. visual inspection, is executed manually by human inspectors, and thus, its accuracy depends on personnel's...
Over the last few decades, the use of electroencephalography (EEG) signals for motor imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep learning have also gained widespread attention and used in various application such as natural language processing, computer vision and speech processing. However, deep learning has been rarely used for MI EEG signal classification...
Fuzzy hyper-line segment neural network (FHLSNN) is a hybrid system of fuzzy logic and neural network and is used for pattern classification. It learns patterns in terms of n-dimensional hyper line segment (HLS). Modified fuzzy hyperline segment neural network (MFHLSNN) is a modified version of FHLSNN that improves the quality of reasoning and recall time per pattern using modified fuzzy membership...
Heart disease is the biggest killer in the world, it is a serious public health problem facing the world today. This problem has not only attracted the attention of doctors and cardiologists, but also that of signal processing specialists who seek to effectively detect this disease by treating cardiac signals. This article proposes a heart sounds classification algorithm into two categories (Normal...
In this paper modified enhanced fuzzy min max (modified-EFMMN) has been proposed for pattern classification. The objectives of modified-EFMM are firstly, to lift the classification accuracy, secondly to reduce the network complexity and thirdly to utilize minimum number of features to provide classification decision. The modified-EFMM handles overlap among the different class hyperbox more stringently,...
Incremental Attribute Learning (IAL) is a feasible machine learning strategy for solving high-dimensional pattern classification problems. It gradually trains features one by one, which is quite different from those conventional machine learning approaches where features are trained in one batch. Preprocessing, such as feature selection, feature ordering and feature extraction, has been verified as...
Gaussian mixture models (GMM) remain popular in pattern classification applications due to their well understood Bayesian framework and the availability of good training algorithms such as the expectation maximization (EM) algorithm. EM is a non-discriminative training algorithm. The performance of a GMM trained with the EM algorithm can often fall short of other discriminative pattern classification...
In order to extract effective audio feature using autoencoder, different from traditional bottle-neck autoencoder, bottle-body autoencoder is presented in this paper, which is constructed using restricted Boltzmann machine with the same neurons at every layer. Bottle-body feature, which is obtained by using pseudo-inverse method to initialize weights, is applied to audio signal classification. The...
Contextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest classifier together with Markov Random Fields (MRF) that surpasses its naïve version, which was recently presented in the literature. The experimental results over four satellite...
Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features...
In this paper, we investigate neural network ensemble (NNE) classifier and its application to multi-spectral image classification. The effectiveness of the NNE classifier is demonstrated on SPOT multi-spectral image data. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, k-NN classifier, it has shown that the NNE classifier can have better performance on multi-spectral...
In pattern classification or machine learning, instance-based learning (IBL) has gained much attention and can yield superior performance in many domains. In IBL, however, the storage requirement is proportional to the number of training instances. Furthermore, it usually takes too much time to classify a new, unseen instance because all training instances need to be considered in determining the...
Irrigation in agricultural lands plays a vivacious role in water and soil conservation. Future prediction of soil moisture content using real-time soil and environmental parameters may provide an efficient platform for agriculture land irrigation requirements. In this paper we have proposed one optimization technique like Gradient Descent with Momentum is used to train neural network pattern classification...
This paper proposes a non-Gaussian approach for biosignal classification based on the Johnson SU translation system. The Johnson system is a normalizing translation that transforms data without normality to normal distribution using four parameters, thereby enabling the representation of a wide range of shapes for marginal distribution with skewness and kurtosis. In this study, a discriminative model...
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