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Requirement Engineering is now established is the most critical phase in software engineering and development due to the fact that success or failure of any project depends greatly on wise and correct decisions made and strategies adopted during this phase. However, the great irony is that most of the requirement engineering still relies on outdated, manual and flawed strategies of past decades. The...
Security of computers and the networks that connect them is increasingly becoming of great significance. As an effect, building effective intrusion detection models with good accuracy and real-time performance are essential. In this paper we propose a new data mining based technique for intrusion detection using Cost-sensitive classification and Support Vector Machines. We introduced an algorithm...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
Applications of neural network were widely used in construct project cost estimate. Aim at handling weakness of poor convergence and insufficient forecast, an improved fuzzy neural network method based on SOFM (self-organizing feature map) and GA (genetic algorithm) was proposed to replace the fashionable T-S fuzzy neural network. The method illustrated how to apply SOFM and GA to improve the fault...
In this paper we have proposed a new way to achieve the optimum learning rate that can reduce the learning time of the multi layer feed forward neural network. The effect of optimum numbers of inner iterations and numbers of hidden nodes on learning time and recognition rate has been shown. The Principal Component Analysis and Multilayer Feed Forward Neural Network are applied in face recognition...
This paper presents the results from a neural network rule extraction algorithm applied to the LED display recognition problem. We show that pruned neural networks with small number of hidden nodes and connections are able to recognize all the 10 digits from 0 to 9. Earlier work by other researchers demonstrated how symbolic fuzzy rules can be extracted from trained neural networks to solve this problem...
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems...
Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or to increase the ensemble's accuracy. This paper focuses on instance-based approaches to ensemble pruning, where a different subset of the ensemble may be used for each different unclassified instance. We propose modeling...
Instance-based learning algorithms typically suffer influences of dissimilarity functions. The problem is frequently related to the Nearest Neighbor rules of these algorithms. This paper will introduce a new dissimilarity measure, called Heterogeneous Centered Difference Measure, which is tested over many known databases. The results are compared with other distance functions.
Aimming at the ever-present problem of imbalanced data in text classification, the authors study on several forms of imbalanced data, such as text number, class size, subclass and class fold. Some useful conclusions are gotten from a series of correlative experiments: first, when the text of two class is almost the same number, the difference of word number become major factor to affect the accuracy...
This work studies the use of Particle Swarm Optimization (PSO) as a classification technique. Beyond assessing classification accuracy, it investigates the following questions: does PSO present limitations for high dimensional application domains? Is it less efficient for multi class problems? To answer the questions, an experimental set up was realized that uses three high dimensional data sets....
An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of PRBF is a weighted...
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Provost (2007) proposed the weighted-vote relational neighbor classifier (wvRN) as a simple yet effective baseline for semi-supervised learning on network data. It is similar to many recent graph-based SSL methods and is shown...
Recently, a huge wave of social media has generated significant impact in people's perceptions about technological domains. They are captured in several blogs/forums, where the themes relate to products of several companies. One of the companies can be interested to track them as resources for customer perceptions and detect user sentiments. The keyword-based approaches for identifying such themes...
Recent advances in Wireless Sensor Networks (WSNs) make them more important to apply. Therefore, security issues are more significant in WSNs. WSNs are susceptible to some types of attacks since they are consisted of cheap and small devices and are deployed in open and unprotected environments. In this research, an Intrusion Detection System (IDS) created in cluster head is proposed. The proposed...
The cancer classification through gene expression patterns becomes one of the most promising applications of the microarray technology. It is also a significant procedure in bioinformatics. In this study a grid computing based evolutionary mining approach is proposed as discriminant function for gene selection and tumor classification. The proposed approach is based on the grid computing infrastructure...
The Internet's numerous benefits have always been coupled with shortcomings due to the abuses of online anonymity. Writeprint identification is a technique to identify individuals based on textual identity cues people leave behind online messages. Character n-gram is one of the most effective approaches to identify writeprint according to previous research. In this study, we propose a variable length...
In this paper classification of chip form and main cutting force prediction of cast nylon in turning operation by using artificial neural network (ANN) are described. The multi-layer perceptron of back-propagation neural network (BPNN) was employed as a tool to classify a chip form following ISO 3685-1977(E) and predicted the tangential cutting force. The turning operation was performed by a conventional...
Vocabulary Tree (VT) is one of offline learning-classifier to deal with large number of image set efficiently by combining bag of words concept with tree structure. Bag of words concept makes our classifier possible to return robust classification results regardless of image size, rotation, and other noises. Tree structure can give us very fast classification testing time. But, because of limitation...
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