The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Consistency of ignition sensitivity of electric-detonator is the key to series simultaneous blasting in engineering. To tackle the problem of inaccurate measurement of sensitivity, based on the electro-thermal response principle, a work of analyzing the characteristic parameters obtained by the nondestructive testing technique was carried on, a mathod was found to set up the fuzzy pattern of product's...
Classification is the most researched topic of neural networks. There are a great number of literature that analyze application of neural networks as a method of classification in different spheres of humans' life. This paper shows the general review of these spheres. Examples include many problems in business, science and medicine, that can be solved by the neural networks algorithms of classification.
Boolean logic interpretations, as well as multiple-valued logic extensions, have been recently proposed for analogical proportions (i.e. statements of the form “a is to b as c is to d”), and for two other related formal proportions named reverse analogy (“what a is to b is the reverse of what c is to d”), and paralogy (“what a and b have in common c and d have it also”). These proportions relate items...
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original...
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) sub-types classification, we propose a novel integrative scheme utilizing PPI network, functional gene sets information, and mRNA profiling. The workflow of the proposed scheme includes three major steps: First, by combining protein-protein interaction network structure and gene co-expression relationship into...
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...
The advantage of artificial neural network is that there is no need to devise a mathematical model in order to perform a specific task. It processes information through interconnected processing elements (neurons). In assessment of environmental quality, ANN is an efficient and objective classification method. However according to our experience, ANN produces are not always comply with real situation,...
Gene selection from microarray data is an important issue for gene expression based classification and to carry out a diagnostic test. In this regard, a rough set based gene selection algorithm is presented. It selects the set of genes by maximizing the relevance and significance of the genes, which are calculated based on the theory of rough sets. Using the predictive accuracy of K-nearest neighbor...
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
In this paper, a new Hierarchical fuzzy classifier based on evolutionary boosting algorithms is proposed. The main goal of this paper is to improve the performance of fuzzy rule based classifiers through utilizing hierarchical structure for achieving fuzzy rules. The advantages of hierarchical fuzzy rules generated by evolutionary boosting algorithms are evaluated by comparison between the performance...
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...
The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the framework...
Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyper plane. We propose an extension for linearly non-separable datasets that allows a direct trade-off between the number of misclassified data points and the number of dimensions. Results on toy examples as well as...
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a...
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,...
Classifier selection aims to reduce the size of an ensemble of classifiers in order to improve its efficiency and classification accuracy. Recently an information-theoretic view was presented for feature selection. It derives a space of possible selection criteria and show that several feature selection criteria in the literature are points within this continuous space. The contribution of this paper...
The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated...
The use of patterns in predictive models has received a lot of attention in recent years. This paper presents a pattern-based classification model which extracts the patterns that have similarity among all objects in a specific class. This introduced model handles the problem of the dependence on a user-defined threshold that appears in the pattern-based subspace clustering. The experimental results...
In this paper, we study the probability of using heart sound as a biometric for human authentication. The most significant contribution of using heart sound as a biometric is that it cannot be easily replicated as compared to other conventional biometrics. The proposed Heart Sound Authentication System (HSAS) consists of five main phases, namely, Heart Sound Capturing, Pre-processing, Feature Extraction,...
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...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.