scholarly journals Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Lev V. Utkin ◽  
Yulia A. Zhuk
Keyword(s):  
Partial Information ◽  
Probability Distributions ◽  
Classification Problem ◽  
Support Vector ◽  
Classification Problems ◽  
Mean Values ◽  
Numerical Examples ◽  
The Standard Model ◽  
The Mean ◽  
Available Information

A method for solving a classification problem when there is only partial information about some features is proposed. This partial information comprises the mean values of features for every class and the bounds of the features. In order to maximally exploit the available information, a set of probability distributions is constructed such that two distributions are selected from the set which define the minimax and minimin strategies. Random values of features are generated in accordance with the selected distributions by using the Monte Carlo technique. As a result, the classification problem is reduced to the standard model which is solved by means of the support vector machine. Numerical examples illustrate the proposed method.

scholarly journals Spectrogram Classification Using Dissimilarity Space

2020 ◽  
Vol 10 (12) ◽  
pp. 4176 ◽  
Author(s):  
Loris Nanni ◽  
Andrea Rigo ◽  
Alessandra Lumini ◽  
Sheryl Brahnam
Keyword(s):  
Ad Hoc ◽  
Classification Problem ◽  
Space Representation ◽  
Support Vector ◽  
Clustering Methods ◽  
Audio Classification ◽  
Classification Problems ◽  
Clustering Techniques ◽  
Vector Space Representation ◽  
Better Than

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs.

scholarly journals NEUTRINO–ELECTRON PROCESSES IN A DENSE MAGNETIZED PLASMA

2000 ◽  
Vol 15 (25) ◽  
pp. 1551-1556 ◽  
Author(s):  
N. V. MIKHEEV ◽  
E. N. NARYNSKAYA
Keyword(s):  
Neutrino Energy ◽  
Magnetized Plasma ◽  
Total Probability ◽  
Mean Values ◽  
Neutrino Scattering ◽  
The Standard Model ◽  
The Mean ◽  
Energy And Momentum ◽  
Strongly Degenerate ◽  
The Magnetic Field

The neutrino–electron processes in a dense strongly degenerate magnetized plasma are analyzed in the framework of the Standard Model. The total probability and the mean values of the neutrino energy and momentum losses are calculated. It is shown that neutrino scattering on the excited electrons with Landau level number conservation dominates under the conditions μ2 > eB ≫ μT but does not give a contribution into the neutrino force acting on plasma along the magnetic field.

scholarly journals Hierarchical Multi Twin Support Vector Machine

2021 ◽  
Vol 130 (2B) ◽  
Author(s):  
Nguyen The Cuong
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Data Type ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Better Than

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.

scholarly journals Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2215
Author(s):  
Jung-Kai Tsai ◽  
Chih-Hsing Hung
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Classification Problem ◽  
Back Propagation Neural Network ◽  
Future Research ◽  
Support Vector ◽  
Enterprise Performance ◽  
Classification Problems ◽  
Proposed Model ◽  
Multi Class Classification

Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end.

scholarly journals Analysis of a Discrete-Time Queueing Model with Disasters

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3283
Author(s):  
Mustafa Demircioglu ◽  
Herwig Bruneel ◽  
Sabine Wittevrongel
Keyword(s):  
Discrete Time ◽  
Queueing System ◽  
Time Slot ◽  
Probability Distributions ◽  
Single Server ◽  
Bernoulli Process ◽  
Tail Probabilities ◽  
Mean Values ◽  
The Mean ◽  
The Impact

Queueing models with disasters can be used to evaluate the impact of a breakdown or a system reset in a service facility. In this paper, we consider a discrete-time single-server queueing system with general independent arrivals and general independent service times and we study the effect of the occurrence of disasters on the queueing behavior. Disasters occur independently from time slot to time slot according to a Bernoulli process and result in the simultaneous removal of all customers from the queueing system. General probability distributions are allowed for both the number of customer arrivals during a slot and the length of the service time of a customer (expressed in slots). Using a two-dimensional Markovian state description of the system, we obtain expressions for the probability, generating functions, the mean values, variances and tail probabilities of both the system content and the sojourn time of an arbitrary customer under a first-come-first-served policy. The customer loss probability due to a disaster occurrence is derived as well. Some numerical illustrations are given.

scholarly journals WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE

2021 ◽  
Vol 37 (1) ◽  
pp. 43-56
Author(s):  
Nguyen The Cuong ◽  
Huynh The Phung
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Training Time ◽  
Structural Support

In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the capability of S-TWSVM’s data simulation is better than that of TWSVM. However, for the datasets where each class consists of clusters of different trends, the S-TWSVM’s data simulation capability seems restricted. Besides, the training time of S-TWSVM has not been improved compared to TWSVM. This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

scholarly journals Implementation of TQM and the Integration of BIM in the Construction Management Sector in Saudi Arabia Validated with Hybridized Emerging Harris Hawks Optimization (HHO)

2021 ◽  
Author(s):  
Mohammad Abazid ◽  
Hüseyin Gökçekuş ◽  
Tahir Celik
Keyword(s):  
Saudi Arabia ◽  
Construction Projects ◽  
Evaluation Metrics ◽  
Support Vector ◽  
Relative Importance ◽  
Total Quality ◽  
Mean Values ◽  
Performance Accuracy ◽  
Importance Index ◽  
The Mean

This research is aimed at evaluating two different scenarios, firstly, appraising the impacts of employing the concepts of Total Quality Management (TQM) to the construction projects in Saudi Arabia. The results of the study were obtained through utilization of a descriptive analytical approach, where 300 questionnaires were distributed to engineering firms and companies with a response rate of 200 questionnaires, hence achieving the study sample for this research. The data gathered was analyzed by applying the Statistical Package for Social Science (SPSS) program and calculating the Relative importance index (RII) and the mean values. From the research conducted, the outcomes showed that the management’s ability to commit using TQM while applying BIM obtained a relative importance of (0.717), while the relative importance for the management’s ability to commit using TQM without the application of BIM is (0.552). The results showed that construction projects in Saudi Arabia still sustain setbacks from applying TQM concepts and suffer from the lack of administrative, scientific and technical applications. In a second scenario, a hybridized support vector regression (SVR) Harris-hawks optimization (HHO) (i.e., SVR-HHO) were used to predict the TQM. The performance accuracy of the models was checked through three different evaluation metrics namely; mean square error (MSE), correlation co-efficient (CC) and Nash-Sutcliffe efficiency (NSE). the hybridized emerging SVR-HHO outperformed the other two data driven approaches in both the training and testing stages based on the employed evaluation metrics. Overall, the obtained results showed that both the machine learning and metaheuristic approaches were capable of predicting TQM.

scholarly journals Monitoring the mean with least-squares support vector data description

2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Edgard M. Maboudou-Tchao
Keyword(s):  
Least Squares ◽  
Statistical Process Control ◽  
Control Charts ◽  
Classification Problem ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Statistical Process ◽  
Data Description ◽  
The Mean

Abstract: Multivariate control charts are essential tools in multivariate statistical process control (MSPC). “Shewhart-type” charts are control charts using rational subgroupings which are effective in the detection of large shifts. Recently, the one-class classification problem has attracted a lot of interest. Three methods are typically used to solve this type of classification problem. These methods include the k−center method, the nearest neighbor method, one-class support vector machine (OCSVM), and the support vector data description (SVDD). In industrial applications, like statistical process control (SPC), practitioners successfully used SVDD to detect anomalies or outliers in the process. In this paper, we reformulate the standard support vector data description and derive a least squares version of the method. This least-squares support vector data description (LS-SVDD) is used to design a control chart for monitoring the mean vector of processes. We compare the performance of the LS-SVDD chart with the SVDD and T2 chart using out-of-control Average Run Length (ARL) as the performance metric. The experimental results indicate that the proposed control chart has very good performance.

ML2S-SVM: multi-label least-squares support vector machine classifiers

2019 ◽  
Vol 37 (6) ◽  
pp. 1040-1058 ◽  
Author(s):  
Shuo Xu ◽  
Xin An
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Image Classification ◽  
Classification Problem ◽  
Superior Performance ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Content Type ◽  
Classification Approach

Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

scholarly journals Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Semih Dinç ◽  
Abdullah Bal
Keyword(s):  
Hyperspectral Image ◽  
Classification Problem ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Distributed Data ◽  
Classification Problems ◽  
Novel Approach ◽  
Training Stage ◽  
Improved Performance ◽  
Two Stages

This paper presents a novel approach for the hyperspectral imagery (HSI) classification problem, using Kernel Fukunaga-Koontz Transform (K-FKT). The Kernel based Fukunaga-Koontz Transform offers higher performance for classification problems due to its ability to solve nonlinear data distributions. K-FKT is realized in two stages: training and testing. In the training stage, unlike classical FKT, samples are relocated to the higher dimensional kernel space to obtain a transformation from non-linear distributed data to linear form. This provides a more efficient solution to hyperspectral data classification. The second stage, testing, is accomplished by employing the Fukunaga- Koontz Transformation operator to find out the classes of the real world hyperspectral images. In experiment section, the improved performance of HSI classification technique, K-FKT, has been tested comparing other methods such as the classical FKT and three types of support vector machines (SVMs).

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