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Every classification model contains uncertainty. This uncertainty can be distributed evenly or into certain areas of feature space. In regular classification tasks, the uncertainty can be estimated from posterior probabilities. On the other hand, if the data set contains missing values, not all classifiers can be used directly. Imputing missing values solves this problem but it suppresses variation...
A method for quadratic hybrid fuzzy least-squares regression is developed in this paper. Input and output information is presented in the form of trapezoidal fuzzy numbers. The method of regressions creation is based on the transformation of the input and output fuzzy numbers into intervals, which are called weighted intervals. The proposed method extends a group of initial data membership functions...
This work presents a new approach based on support vector regression to deal with incomplete input (unseen) data and compares it to other existing techniques. The empirical analysis has been done over 18 real data sets and using five different classifiers, with the aim of foreseeing which technique can be deemed as more suitable for each classifier. Also, this study tries to devise how the relevance...
Coreference is a common linguistic phenomenon in natural language understanding, it plays an important role in simplifying the expression and linking up the context. In this paper, the algorithm of support vector machines is applied to solve the problem of Chinese coreference, we consider fully the important characteristics which related to coreference and integrate them effectively to build model...
Many machine learning algorithms require a single value per feature per record for modeling. However, there are applications, in the medical domain particularly, where a single record may have multiple observations for the same feature. For example, a patient could have the same gene analyzed in multiple tissue slides of a biopsy, or could have the same genetic test performed on multiple subsequent...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
We introduce a class of statistics for characterizing the periphery of a distribution, and show that these statistics are particularly valuable for problems in target detection. Because so many detection algorithms are rooted in Gaussian statistics, we concentrate on ellipsoidal models of high-dimensional data distributions (that is to say: covariance matrices), but we recommend several alternatives...
Reservoir modeling is an on-going activity during the production life of a reservoir. One challenge to constructing accurate reservoir models is the time required to carry out a large number of computer simulations. To address this issue, we have constructed surrogate models (proxies) for the computer simulator to reduce the simulation time. The quality of the proxies, however, relies on the quality...
A new data reconciliation algorithm based on least squares support vector machines (LSSVM) for nonlinear dynamic process is proposed in this work. Firstly, response of processes and training data is obtained by computation tools or simulation software. Secondly, the local models of processes are identified by LSSVM. Finally, process data reconciliation is transformed to nonlinear program problem with...
Customer classification is a key step in customer relationship management (CRM), and there are many methods used for it, such as Neural Net, association rules, SOM model, etc. However, most existing methods don't take noise which is very common in reality into consideration. In this paper, we combine Croup Method of Data Handling (GMDH) with Takagi and Sugeno fuzzy model (TS) to form a new classification...
Unbalanced data, minority classes with few samples, present in many applications. It is difficult to solve the problems of unbalanced data by traditional methods. In this paper, a hybrid algorithm based on random over-sampling, decision tree (DT), particle swarm optimization (PSO) and feature selection is proposed to classify unbalanced data. The proposed algorithm has the ability to select beneficial...
This paper presents a novel approach to eliminate the effect of noisy samples from the learning step of support vector data description (SVDD) method. SVDD is a popular kernel method which tries to fit a hypersphere around the target object and can obtain more flexible and more accurate data descriptions by using proper kernel functions. Nonetheless, the SVDD could sometimes generate such a loose...
Temper rolling is essential for the quality of steel sheets. The degree of temper rolling determines the mechanical properties of the steel sheet and is highly influenced by the rolling force or strip tension. Since mathematical models generate unsatisfactory results for the calculation of these two process parameters, other methods for the presetting of tempers mills must be used. The parameter presetting...
In recent years, anonymization methods have emerged as an important tool to preserve individual privacy when releasing privacy sensitive data sets. This interest in anonymization techniques has resulted in a plethora of methods for anonymizing data under different privacy and utility assumptions. At the same time, there has been little research addressing how to effectively use the anonymized data...
We consider the problem of semi-supervised learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better utilize the information from unlabeled data by formulating them as a three-class (-1,+1, 0) mixture, where class 0 represents the irrelevant data. This distinguishes our work from the traditional SSL problem where unlabeled...
In this paper, we propose a scheme to improve the performance of subspace learning by using a pattern (data) selection method as preprocessing. Generally, a training set for subspace learning contains irrelevant or unreliable samples, and removing these samples can improve the learning performance. For this purpose, we use pattern selection preprocessing which discriminates decision boundary/non-boundary...
The paper proposes a hybrid methodology that exploits the unique strength of the autoregressive integrated moving average model and the support vector machine model in forecasting time series. The simulation experiment results showed that the hybrid model is superior to the individual models for the test values of the turbo-generator vibration. Most of the individual models evaluated showed poor ability...
Is it possible to identify and even forecast well in advance (6-12 months) the relative stability of a state to enable policy makers to successfully intervene? How does one acquire that understanding? One technique is to model and understand the social factors, which summarize the background conditions, attributes and performance factors of the country over time. The purpose of this paper is to: (1)...
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