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Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions...
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...
Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a...
An on-line BSE algorithm with an adaptive learning rate is proposed. By indirectly studying one of the existing on-line BSE algorithms based on line predictability, the bound for the optimal learning rate which guarantees the convergence of the algorithm is derived. Based on the analysis results, an on-line algorithm with an adaptive learning rate is presented. Since the learning rates of the existing...
kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice and plays an important role in pattern classification. However, how to choose an optimal value of k is still a challenge, which straightforwardly affects the performance of kNN. To alleviate this problem, in this paper we propose a new learning algorithm under the framework of kNN. The primary...
Cloud computing is an elastic computing model that the users can lease the resources from the rentable infrastructure. Cloud computing is gaining popularity due to its lower cost, high reliability and huge availability. To utilize the powerful and huge capability of cloud computing, this paper is to import it into data mining and machine learning field. As one of the most influential and open competition...
Text categorization is one important task of text mining, for automated classification of large numbers of documents. Many useful supervised learning methods have been introduced to the field of text classification. Among these useful methods, K-Nearest Neighbor (KNN) algorithm is a widely used method and one of the best text classifiers for its simplicity and efficiency. For text categorization,...
Previous work has shown that application of graph mining techniques to system dependence graphs improves the precision of automatic defect discovery by revealing subgraphs corresponding to implicit programming rules and to rule violations. However, developers must still confirm, edit, or discard reported rules and violations, which is both costly and error-prone. In order to reduce developer effort...
Random forest is an excellent ensemble learning method, which is composed of multiple decision trees grown on random input samples and splitting nodes on a random subset of features. Due to its good classification and generalization ability, random forest has achieved success in various domains. However, random forest will generate many noisy trees when it learns from the data set that has high dimension...
We have established a multi-walker recognition/tracking testbed based on low-cost pyroelectrc sensor network (PSN). In order to identify a region of interest (Rol) in the monitoring area for the detection of any interesting mobile targets, we propose to use Bayesian machine learning and binary signal projection to extract the statistical contextual features from real-time, high-dimensional PSN data...
We study the vulnerability reports in the Common Vulnerability and Exposures (CVE) database by using topic models on their description texts to find prevalent vulnerability types and new trends semi-automatically. In our study of the 39,393 unique CVEs until the end of 2009, we identify the following trends, given here in the form of a weather forecast: PHP: declining, with occasional SQL injection...
A consensus feature-ranking approach has been applied to the study of localization-related temporal lobe epilepsy (TLE) in order to evaluate the relative discriminative power of individual attributes. Cases were selected on the basis of a postoperative outcome free of disabling seizures (i.e., Engel class I) in order to establish a definitive laterality of focal epileptogenicity. Several quantitative...
This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence...
Computer games are attracting increasing interest in the Artificial Intelligence (AI) research community, mainly because games involve reasoning, planning and learning. One area of particular interest in the last years is the creation of adaptive game AI. Adaptive game AI is the implementation of AI in computer games that holds the ability to adapt to changing circumstances, i.e., to exhibit adaptive...
Performance of classification methods using Machine Learning Techniques majority depends on the quality of data were used in learning. The transformation techniques are used to increase the efficiency of classification because each type of data is suitable for different classification techniques. This study is aimed at providing comparative performance of different classification techniques by changing...
Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires...
Mining data has attracted many researchers because of its usefulness of extracting valuable information from the huge volume of continuously increasing databases. In general using labeled data has been more difficult and time consuming than using unlabeled samples. There are several methods that could be used to build a classifier using unlabeled samples. However these may suffer from poor classification...
Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector Machine...
In this paper, a modified XCS is proposed to reduce the numbers of learned rules. XCS is a type of learning classifier systems and has been proven able to find accurate, maximal generalizations. However, XCS usually produces too many rules such that the readability of the classification model is greatly reduced. As a result, XCS users may not be able to obtain the desired knowledge or useful information...
Classical intrusion detection system tends to identify attacks by using a set of rules known as signatures defined before the attack, this kind of detection is known as misuse intrusion detection. But reality is not always quantifiable, and this drives us to a new intrusion detection technique known as anomaly intrusion detection, due to the difficulties of defining normal pattern for random data...
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