Number Of Relevant Features Research Articles

AdStop: Efficient flow-based mobile adware detection using machine learning

In recent years, mobile devices have become commonly used not only for voice communications but also to play a major role in our daily activities. Accordingly, the number of mobile users and the number of mobile applications (apps) have increased exponentially. With a wide user base exceeding 2 billion users, Android is the most popular operating system worldwide, which makes it a frequent target for malicious actors. Adware is a form of malware that downloads and displays unwanted advertisements, which are often offensive and always unsolicited. This paper presents a machine learning-based system (AdStop) that detects Android adware by examining the features in the flow of network traffic. The design goals of AdStop are high accuracy, high speed, and good generalizability beyond the training dataset. A feature reduction stage was implemented to increase the accuracy of Adware detection and reduce the time overhead. The number of relevant features used in training was reduced from 79 to 13 to improve the efficiency and simplify the deployment of AdStop. In experiments, the tool had an accuracy of 98.02% with a false positive rate of 2% and a false negative rate of 1.9%. The time overhead was 5.54 s for training and 9.36 µs for a single instance in the testing phase. In tests, AdStop outperformed other methods described in the literature. It is an accurate and lightweight tool for detecting mobile adware.

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods.

Operational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the relationship between different variables changes over time and identifies different modes of operation. Gas turbines’ operational modes are usually defined regarding their expected energy production, and most research works either are focused a priori on obtaining these modes solely based on one variable, the active load, or assume a fixed number of states and build up predictive models to classify new situations as belonging to the predefined operational modes. However, in this work, we take into account all available parameters based on sensors’ data because other factors can influence the system status, leading to the identification of a priori unknown operational modes. Furthermore, for gas turbine management, a key issue is to detect these modes using a real-time monitoring system. Our approach is based on using unsupervised machine learning techniques, specifically an ensemble of clusters to discover consistent clusters, which group data into similar groups, and to generate in an automatic way their description. This description, upon interpretation by experts, becomes identified and characterized as operational modes of an industrial process without any kind of a priori bias of what should be the operational modes obtained. Our proposed methodology can discover and identify unknown operational modes through data-driven models. The methodology was tested in our case study with Siemens gas turbine data. From available sensors’ data, clusters descriptions were obtained in an automatic way from aggregated clusters. They improved the quality of partitions tuning one consistency parameter and excluding outlier clusters by defining filtering thresholds. Finally, operational modes and/or sub-operational modes were identified with the interpretation of the clusters description by process experts, who evaluated the results very positively.

A Decision Tree-Aware Genetic Algorithm for Botnet Detection

In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from the whole features set. Thus, it obtains efficient botnet detection results in terms of F-score, precision, detection rate, and number of relevant features, when compared with DT alone.

GABC

Feature selection is a complex pre-processing step in data mining that enhances classification accuracy by selecting the minimum number of relevant features. Artificial bee colony algorithm (ABC) is one of the successful swarm intelligent algorithms for feature selection, image processing, data analytics, protein structure prediction, etc. It simulates the honey foraging behavior of the bee swarm. But it tends to low convergence speed and local optima stagnation. Hybrid meta-heuristics can enhance the performance of existing swarm algorithms. This paper proposes a hybrid approach for the ABC algorithm by incorporating genetic operators into it. The mutation operator is used to explore the better-quality neighborhood while the crossover is used to enhance the quality of solutions by implementing diversity into them. The performance of the proposed method is evaluated using UCI data sets and compared with existing swarm algorithms for feature selection. The effectiveness of the proposed method is evident from the results.

An evolutionary decomposition-based multi-objective feature selection for multi-label classification.

Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each entity has more than one label. To improve the performance of multi-label classification, feature selection plays an important role. It involves identifying and removing irrelevant and redundant features that unnecessarily increase the dimensions of the search space for the classification problems. However, classification may fail with an extreme decrease in the number of relevant features. Thus, minimizing the number of features and maximizing the classification accuracy are two desirable but conflicting objectives in multi-label feature selection. In this article, we introduce a multi-objective optimization algorithm customized for selecting the features of multi-label data. The proposed algorithm is an enhanced variant of a decomposition-based multi-objective optimization approach, in which the multi-label feature selection problem is divided into single-objective subproblems that can be simultaneously solved using an evolutionary algorithm. This approach leads to accelerating the optimization process and finding more diverse feature subsets. The proposed method benefits from a local search operator to find better solutions for each subproblem. We also define a pool of genetic operators to generate new feature subsets based on old generation. To evaluate the performance of the proposed algorithm, we compare it with two other multi-objective feature selection approaches on eight real-world benchmark datasets that are commonly used for multi-label classification. The reported results of multi-objective method evaluation measures, such as hypervolume indicator and set coverage, illustrate an improvement in the results obtained by the proposed method. Moreover, the proposed method achieved better results in terms of classification accuracy with fewer features compared with state-of-the-art methods.

Contralateral delay activity does not reflect behavioral feature load in visual working memory

An ongoing debate in visual working memory research is concentrated on whether visual working memory capacity is determined solely by the number of objects to be memorized, or additionally by the number of relevant features contained within objects. Using a novel change detection task that contained multi-feature objects we examined the effect of both object number and feature number on visual working memory capacity, change detection sensitivity, and posterior slow wave event-related brain potential (ERP) activity. Behaviorally, working memory capacity and sensitivity were modulated as a function of both the number of objects and the number of features memorized per object. However, the Contralateral Delay Activity (CDA) was only sensitive to the number of objects, but not to the number of features. This suggests that while both objects and features contribute to limitations in visual working memory capacity, the CDA is an insufficient mechanism to account for feature level representations.

A Method for Anomalies Detection in Real-Time Ethernet Data Traffic Applied to PROFINET

There are major discussions about the vulnerability of protocols based on real-time Ethernet (RTE) and techniques for detecting anomalies. Thus, this work proposes a methodology for detecting anomalies by optimizing the data extraction and by classifying traffic-related features. In order to cope with this proposal, an artificial neural network (ANN)-based classifier is trained using selected relevant features. These features are extracted using variable sized sliding window and selected according to their correlation with the other features and the expected output of the classifier. The number of relevant features can vary according to performance indicators of the classifier. The proposed methodology was exploited for identifying four different events of PROFINET networks. The performance of the ANN-based classifier was considered successful for all cases. This outcome suggests that the proposed methodology may be successful for anomalies detection in any PROFINET network. However, the application of the proposed methodology to other RTE protocol is foreseen.

Correlation based feature selection with clustering for high dimensional data

Feature selection is an essential technique to reduce the dimensionality problem in data mining task. Traditional feature selection algorithms are fail to scale on large space. This paper proposes a new method to solve dimensionality problem where clustering is integrating with correlation measure to produce good feature subset. First Irrelevant features are eliminated by using k-means clustering method and then non-redundant features are selected by correlation measure from each cluster. The proposed method is evaluate on Microarray and Text datasets and the results are compared with other renowned feature selection methods using Naïve Bayes classifier. To verify the accuracy of the proposed method with different number of relevant features, percentagewise criteria is used. The experimental results reveal the efficiency and accuracy of the proposed method.

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases.

Comparative Study on the Performance of MMIFS and DMIFS Feature Selection Algorithms on Medical Datasets

Background/Objectives: Feature selection is one of the preprocessing techniques used for removing redundant and irrelevant features. The objective of this research work is to show that small set of relevant features can improve the performance of classification algorithms. Methods/Statistical Analysis: This paper compares the performance of two feature selection algorithms, Modified Mutual Information based Feature Selection (MMIFS) and Dynamic Mutual Information based Feature Selection (DMIFS) The performance of these feature selection algorithms on the medical datasets is analyzed. The performances of c4.5 classification algorithm before and after feature selection are analyzed. Findings: The comparative study show that the feature selection algorithms have selected prominent features of the medical datasets. The percentage of feature reduction and the improvement in the accuracy of the classification algorithm are used for validation. The result shows an improvement in the accuracy of the classification algorithm. Applications/ Improvements: The reduction in the number of features means diagnosis of the disease with limited number of relevant features. Integrating feature selection techniques and machine learning algorithms will give a better decision making tool which is appreciable in medical domain.

CnvCurator: an interactive visualization and editing tool for somatic copy number variations.

BackgroundOne of the most important somatic aberrations, copy number variations (CNVs) in tumor genomes is believed to have a high probability of harboring oncotargets. Detection of somatic CNVs is an essential part of cancer genome sequencing analysis, but the accuracy is usually limited due to various factors. A post-processing procedure including manual review and refinement of CNV segments is often needed in practice to achieve better accuracy.ResultscnvCurator is a user-friendly tool with functions specifically designed to facilitate the process of interactively visualizing and editing somatic CNV calling results. Different from other general genomics viewers, the index and display of CNV calling results in cnvCurator is segment central. It incorporates multiple CNV-specific information for concurrent, interactive display, as well as a number of relevant features allowing user to examine and curate the CNV calls.ConclusionscnvCurator provides important and practical utilities to assist the manual review and edition of results from a chosen somatic CNV caller, such that curated CNV segments will be used for down-stream applications.

Feature selection when there are many influential features

Recent discussion of the success of feature selection methods has argued that focusing on a relatively small number of features has been counterproductive. Instead, it is suggested, the number of significant features can be in the thousands or tens of thousands, rather than (as is commonly supposed at present) approximately in the range from five to fifty. This change, in orders of magnitude, in the number of influential features, necessitates alterations to the way in which we choose features and to the manner in which the success of feature selection is assessed. In this paper, we suggest a general approach that is suited to cases where the number of relevant features is very large, and we consider particular versions of the approach in detail. We propose ways of measuring performance, and we study both theoretical and numerical properties of the proposed methodology.

Individual differences in the effect of relevant concreteness on learning and transfer of a mathematical concept

The concreteness of training materials influences learning and—perhaps more importantly—transfer. Building on prior research finding abstract representations best facilitated transfer to a game task, we conducted a similar study using training figures varying in concreteness but directly assessed transfer to modular arithmetic problems. Training figures: (a) were purely abstract, (b) were abstract but with features relevant to the transfer task, or (c) included additional concrete-relevant features. We hypothesized that concreteness—or number of relevant features—would be positively correlated with learning and transfer—especially among younger and/or lower-ability students. Although there was no overall difference in initial learning, the concrete-relevant and abstract-relevant features independently facilitated near-transfer, where concrete-relevant features supported lower-reasoning students. For far-transfer, eighth-graders benefited from the abstract-relevant features, whereas sixth-graders required additional concrete-relevant features. These findings suggest that concreteness interacts with learner and task characteristics to produce learning and transfer outcomes.

Avoiding foolish consistency

In most cases, rule-governed relations and similarity relations can indeed be distinguished by the number of relevant features they require. This criterion is not sufficient, however, to explain other properties of the relations that have a more dichotomous character. I focus on the differential drive for consistency by inferential processes that draw on the two types of relations.

Direct identification of pure Penicillium species using image analysis

This paper presents a method for direct identification of fungal species solely by means of digital image analysis of colonies as seen after growth on a standard medium. The method described is completely automated and hence objective once digital images of the reference fungi have been established. Using a digital image it is possible to extract precise information from the surface of the fungal colony. This includes color distribution, colony dimensions and texture measurements. For fungal identification, this is normally done by visual observation that often results in a very subjective data recording. Isolates of nine different species of the genus Penicillium have been selected for the purpose. After incubation for 7 days, the fungal colonies are digitized using a very accurate digital camera. Prior to the image analysis each image is corrected for self-illumination, thereby gaining a set of directly corresponding images with respect to illumination. A Windows application has been developed to locate the position and size of up to three colonies in the digitized image. Using the estimated positions and sizes of the colonies, a number of relevant features can be extracted for further analysis. The method used to determine the position of the colonies will be covered as well as the feature selection. The texture measurements of colonies of the nine species were analyzed and a clustering of the data into the correct species was confirmed. This indicates that it is indeed possible to identify a given colony merely by macromorphological features. A classifier (in the normal distribution) based on measurements of 151 colonies incubated on yeast extract sucrose agar (YES) was used to discriminate between the species. This resulted in a correct classification rate of 100% when used on the training set and 96% using cross-validation. The same methods applied to 194 colonies incubated on Czapek yeast extract agar (CYA) resulted in a correct classification rate of 98% on the training set and 71% using cross-validation.

Socialinių utopijų tipologizacija

Straipsnyje siekiama apžvelgti pagrindines utopijų rūšis ir pamėginti tipologizuoti socialines utopijas bei aptarti būdingiausius utopijos sampratos aspektus. Šiai sąvokai dažnai suteikiama svajonės, chimeriško kūrinio, kur nesiskaitoma su faktais, nerealaus projekto reikšmė, taip pat socialinės utopijos suvokiamos ir kaip pažiūrų sistemos, pagrįstos esamų socialinių santykių priešpriešinimu kokybiškai kitokiems santykiams, adekvačiau atspindintiems žmonių reikmes. Utopijas galima skirstyti pagal jų santykį su socialinių klasių, grupių bei sluoksnių aspiracijomis (vergvaldžių, valstiečių, buržuazijos, proletariato ir kt. utopijos), pagal pagrindinę jų autorius dominančią problemą (pvz., utopinis socializmas). Dar skiriamos eskapistinės, herojinės, vietos, laiko (retrospektyvinės ir perspektyvinės), metafizinės (racionalistinės), sektos (vienuolyno), politinės utopijos bei antiutopijos. Bendras visų utopistų bruožas – įsitikinimas, kad visuomenei pertvarkyti būtina sukurti ar surasti tokią žmonių grupę, kurios gyvenime dominuoja visai visuomenei būtinas, bet jai dar svetimas, nežinomas idealas.