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.
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...
Aiming to increase the proportion of the samples that has been determinate classified in Naive Credal Classifier, this paper improves conservative inference rule and proposes an incomplete data classification model based on relaxed conservative inference rule. Simulation results of comparative experiment with Naive Bayesian Classifier and Naive Credal Classifier verify the effectiveness of this classification...
Aiming at the shortages of the existing data-mining model for forecasting the industry security, a classification model based on rough sets and BP neural network (BPNN) is put forward in this paper. First, the theory of rough set is applied to pick up and reduce the index attributes. Then, the training samples are sent to the BPNN to train and learn. After that, the sorts of the coal industry security...
To solve the difficulty of the field of Automatic Entity Relation Extraction, in this paper, a method that used binary classification thinking, meanwhile combined with reasoning rules to extract the field of entity relation is proposed. considering comprehensively the context information of entity, entity type and their combination of characteristics to construct the feature set, which in order to...
Data mining or Knowledge discovery is seen as an increasingly important tool by modern business to transform data into an informational advantage. Mining is a process of finding correlations among dozens of fields in large relational databases and extracts useful information that can be used to increase revenue, cuts costs, or both. Classification is a supervised machine learning procedure and an...
This paper presents Perturbed Frequent Itemset based Classification Technique (PERFICT), a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items. They are not very effective in general for relational data that typically contains real valued attributes...
A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms...
In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users...
Within this paper we introduce a framework for semi- to full-automatic discovery and acquisition of bag-of-words style interest profiles from openly accessible Social Web communities. To do such, we construct a semantic taxonomy search tree from target domain (domain towards which we're acquiring profiles for), starting with generic concepts at root down to specific-level instances at leaves, then...
Sensors are being deployed to improve border security generating enormous collections of data and databases. Unfortunately these sensors can respond to a variety of stimuli, sometimes reacting to meaningful events and sometimes triggered by random events which are considered false alarms. The intent of this project is to supplement human intelligence in a sensor network framework that can assist in...
The algorithm to find the novelty temporal pattern from the temporal database is presented. The basic idea of the algorithm is firstly to extract the feature sequence from a time series, then to compare the feature points of the time series with ones of the normal pattern to decide whether there is a novelty pattern in the time series. The temporal relation of time series is reserved in the feature...
The decision tree algorithm is a hot point in the field of data mining, which is usually used to form classifiers and prediction models. In practice, it has a wide application. This paper describes the decision tree technology and its development process, focuses on typical decision tree algorithms, analyzes their advantages and disadvantages, compares several algorithms, and finally discusses the...
Frequent patterns mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent pattern mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called SMFPM to discover weighted frequent patterns over data streams, the proposed method is based...
Identifying the subjective relationship is important for opinion mining on product reviews in Chinese. The commonly used identification methods adopt classifiers as the identifier. However, it is difficult to maintain high accuracy and high recall rate simultaneously for the instability of exciting classification method. Motivated by this, we present a method based on sentential features and ensemble...
To help handle battlefield information superiority to decision superiority (i.e. to rapidly arrive at better decisions than adversaries can respond to), many scientific, technical and technological challenges must be addressed. The most critical of those are information fusion and management at different levels, communication. This paper decribes battlefield information as data streams and mining...
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
Indirect immunofluorescence (IIF) with HEp-2 cells has been used to detect antinuclear auto-antibodies (ANA) for diagnosing systemic autoimmune diseases. The aim of this study is to develop an automatic scheme to identify the fluorescence pattern of HEp-2 cell in the IIF images. By using the previously proposed two-staged segmentation method, the similarity-based watershed algorithm with marker techniques...
Though DNA microarray technology simultaneously measures the expression levels of thousands of genes, only a few underlying gene features may account for significant data variation in gene classification problems. Selection of features from huge data set is difficult and so dimension reduction of gene expression data set is essential in order to determining important features, which play key role...
The land use or land cover map depicts the physical coverage of the Earth's terrestrial surface according to its use (viz. vegetation, habitation, water body, bare soil, artificial structures etc.). Land use map generation from remotely sensed images is one of the challenging task of remote sensing technology. In this article, motivated from group forming behaviour of real ants, we have proposed two...
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.