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Over the years significant research has been performed for automated, i.e. machine vision based fabric inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated fabric inspection systems mainly involve two challenging problems, one of which is defect classification. The amount of research done to date to solve the defect classification problem...
This paper presents the classification of ionic concentration using ion-sensitive field-effect transistor (ISFET) sensors with post-processing neural network ensemble. ISFETs are electrochemical potentiometric sensors that produce voltage response indicative of ionic concentration change. However, in the presence of ions of similar charge, the voltage levels tend to be influenced by the interfering...
One of the critical abilities of intelligent agents is making quick, accurate and wise decision within dynamic environments in a reasonable period of time. This paper introduces a new sequence classification method based on positive and negative sequential patterns. The historical log data from previous performance “TAC/Ad auction” has been used for the classification. We have applied Nconf: an “interestingness...
In the framework of Fuzzy Cognitive maps theory, we propose a novel classify algorithm, which is totally different from the traditional classify algorithm. The novel classify algorithm has three main advantages: Firstly, the procedures of the proposed algorithm are more transparent and understandable, and the classify results have shown the relationship between attributes. Secondly, the predefined...
This paper presents a new approach for the classification of non-stationary signal patterns in an electric power network using a modified wavelet transform and neural network. The wavelet transform is phase corrected to yield a new transform known as S-transform, which has an excellent time-frequency resolution characteristic. The phase correction absolutely references the phase of the wavelet transform...
Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation....
Conventional k-means only considers pair wise similarity during cluster assignment, which aims to minimizing the distance of points to their nearest cluster centroids. In high dimensional space like document datasets, however, two points may be nearest neighbors without belonging to the same class. Thus pair wise similarity alone is often insufficient for class prediction in such space. To that end,...
The paper presents an analysis of several preprocessing, feature extraction and classification methods in combination for yielding optimum performance for SAW sensors array based electronic nose systems. It is found that the combination of logarithmic data preprocessing, linear discriminant analysis based feature extraction and support vector machine based classification yields optimum results.
With the development of information science and modern technology, it becomes more important about how to protect privacy information. In this paper, a novel privacy-preserving support vector machine (SVM) classifier is put forward for arbitrarily partitioned data. The proposed SVM classifier, which is public but does not reveal the privately-held data, has accuracy comparable to that of an ordinary...
A diagnosis method basing on neural network classifier, genetic algorithm (GA) and wavelet transform is proposed for a pulse width modulation voltage source inverter. It is used to detect and identify the transistor open-circuit fault. BP neural network (BPNN) is capable of recognition. However, it has shortcomings obviously. These are just advantages of GA, which has ability of global search. Thus...
Abstract-Prediction of protein-proteininteraction sites is very important to the function of a protein and drug design. In this paper, we adequately utilize the characters of ensemble learning, which can improve the accuracy of individual classifier and generalization ability of the system, and propose a new prediction method of protein-protein interaction sites: ensemble learning method based on...
In this research we took an experiment of two feature selection methods - eta square and stepwise methods on two classification models - back propagation neural network (BPNN) and general regression neural network (GRNN) to study the effects on the correctness of firm bankruptcy classification. The correctness includes the average classification correctness and the power of bankruptcy classification...
this paper presents a classification based on support vector machine (SVM) to carry out comprehensive analysis of the ability of enterprises paying debt,reduce the risk of bank to provide a loan. First this paper introduces the main principle of support vector machines to establish data classification model, using historical data for classification. Then collect the financial indices of 80 enterprises...
It is important to study the neural network (NN) when it falls into chaos, because brain dynamics involve chaos. In this paper, the several chaotic behaviors of supervised neural networks using Hurst Exponent (H), fractal dimension (FD) and bifurcation diagram are studied. The update rule for NN trained with back-propagation (BP) algorithm absorbs the function of the form x(1-x) which is responsible...
Classification rules are the interest of most data miners to summarize the discrimination ability of classes present in data. A classification rule is an assertion, which discriminates the concepts of one class from other classes. The most classification rules mining algorithm aims to providing a single solution where multiple solutions exist. Moreover, it does not guarantee the optimal solution and...
The ensemble classifiers train their base classifiers through certain datasets which are generated by some rules. This paper presents algorithm, called MDR, based on membership degree and roughness of rough set to divide the original datasets into two parts. One part is easy to classify while another is hard. Two different base classifiers are trained for fitting them; those two kinds of classifiers...
Classification is a major problem of study that involves formulation of decision boundaries based on the training data samples. The limitations of the single neural network approaches motivate the use of multiple neural networks for solving the problem in the form of ensembles and modular neural networks. While the ensembles solve the problem redundantly, the modular neural networks divide the computation...
Network traffic has been shown on numerous occasions to be self similar under normal conditions. This self similar property is however, lost during anomalous conditions such as device failure, congestion and malicious intrusions. Therefore, this loss of self similarity can be used to detect such events. The Hurst parameter (H) is the most widely accepted parameter for determining self similarity....
In this work we propose a hybrid learning machine, combining artificial neural networks (ANNs) and binary decision trees, to predict quantitative structure activity relationships (QSARs). This approach directly uses the structural cues from chemical compounds and has been validated for the two significant prediction problems, viz. regression and classification. For regression analysis we show the...
Classification, or supervised learning, is one of the major data mining processes. Protein classification focuses on predicting the function or the structure of new proteins. This can be done by classifying a new protein to a given family with previously known characteristics. There are many approaches available for classification tasks, such as statistical techniques, decision trees and the neural...
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