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Feature selection is a fundamental data preprocessing step in data mining, where its goal is removing some irrelevant and/or redundant features from a given dataset. In this paper, we present a clustering based genetic algorithm for feature selection (CGAFS). The proposed algorithm works in three steps. In the first step, Subset size is determined. In the second step, features are divided into clusters...
This paper presents to the improvement of the Significant Matrix [1] that works along with Genetic Algorithm in feature selection of appropriate data for a decision tree structure. This work proposes the reduction of time that cut off the Genetic Algorithm's work times. The new method is proposed in the name “Significant Matrix 2” which is calculated from the relationship between categorical data...
Machine learning techniques are widely used in medical decision support systems. Medical diagnosis helps to obtain different features representing the different variations of the disease. With the help of different diagnostic procedures, it is likely to have relevant, irrelevant and redundant features to represent a disease. Redundant features contribute to the wrong classification of the disease...
Medical databases contain massive volume of clinical data which could provide valuable information regarding diagnosis, prognosis and treatment plan when mining algorithms are used in appropriate manner. The irrelevant, redundant and incomplete data in medical databases makes the extraction of useful pattern a difficult process. Feature selection, a robust data preprocessing method selects attributes...
The increasing number of malware is becoming a serious threat to the private data as well as to the expensive computer resources. Linux is a Unix based machine and gained popularity in recent years. The malware attack targeting Linux has been increased recently and the existing malware detection methods are insufficient to detect malware efficiently. We are introducing a novel approach using machine...
Manufacturing data is an important source of knowledge that can be used to enhance the production capability. The detection of the causes of defects may possibly lead to an improvement in production. However, the production records generally contain an enormous set of features. It is almost impossible in practice to monitor all features at once. This research proposes the feature reduction technique,...
We apply computational intelligence methods to the domain of fault diagnosis of rotating machinery, specifically submersible motor pumps used in offshore oil exploration. We propose distinct feature models to assemble a global feature pool from which the most discriminative information is filtered by feature selection. Statistically robust performance estimation for representative classifier models...
The use of biometric information has been known widely for both person identification and security application. Each person can be identified by the unique characteristics of one or more of person biometrics. One of the biometric characteristics of that a person can be identified by his voice. In this research, we are interested in studying the effect of proper features that are extracted from discrete...
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack, exhausts the resources of server/service and makes it unavailable for legitimate users. With increasing use of online services and attacks on these services, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS attacks has also grown. Different techniques such as data mining, neural network, genetic algorithms,...
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can result in huge loss of data and make resources unavailable for legitimate users. With continuous growth of Internet users and traffic, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS network attacks has also grown. Different techniques such as data mining and pattern recognition are being used...
Flooding based DoS attack represents one of most danger attacks in computer networks. Maximizing the effectiveness of flooding based DoS Attack detection accuracy is the main concerns of many researchers. So, many of them are focusing on increasing the detection effectiveness by features reducing. However, limited research studies have concentrated on investigation the correlation between features...
Malicious users of the internet can launch Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks with the intent of making the throughput of a network next to none. As the types and number of users of the internet increases, the requirement of an effective Intrusion Detection System(IDS) to detect these attacks also increases. Different techniques such as data mining and pattern...
Finding an appropriate set of features from data of high dimensionality for building an accurate classification model is a well-known NP-hard computational problem. Unfortunately in data mining, some big data are not only big in volume but they are described by a large number of features. Many feature subset selection algorithms have been proposed in the past, they are nevertheless far from perfect...
With the growing number of text documents in the Internet, it is difficult for users to search, find, manage and organize information quickly. Normally, text documents are classified manually and it is time-consuming. Text categorization is a process of assigning text documents into a set of fixed predefined categories. The high dimensionality of text documents made it difficult to categorize because...
We propose to detect phishing emails and websites using particle swarm optimization (PSO) trained auto associative neural network (PSOAANN), which is employed as one class classifier. PSOAANN achieved better results when compared to previous efforts. In the study, we also developed a new feature selection method based on the weights from input to hidden layers of the PSOAANN. We compared its performance...
Medical embedded systems hold the promise to improve health outcomes, decrease isolation, reduce health disparities, and substantially reduce costs. In spite of their revolutionary potentials, these systems face a number of challenges in design and architecture that form stumbling blocks in their path to success. On one hand, as the sensor units continue to become more miniaturized, the underlying...
The Nearest Shrunken Centroid (NSC) method, with Prediction Analysis for Microarrays being its most well known implementation, has been widely used as a classification method for high dimensional biomedical data. A threshold value must also be provided in this method as input and normally, this is selected manually on a “trial and error” basis by executing the NSC method many times using a number...
A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect...
The multimodal biometric which is a combination of two or more modalities of biometric is able to give more assurance for the securities of some systems. Feature level fusion has been shown to provide higher-performance accuracy and provide a more secure recognition system. In this paper, we propose a feature level fusion of face features which are the physical appearance of a person in image-based...
Demand for automatic classification of Brain MRI (Magnetic Resonance Imaging) in the field of Diagnostic Medicine is rising. Feature Selection of Brain MRI is critical and it has a great influence on the classification outcomes, however selecting optimal Brain MRI features is difficult. Particle Swarm Optimization (PSO) is an evolutionary meta-heuristic approach that has shown great potential in solving...
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