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检索条件"主题词=Machine Learning Algorithms"

共 26009 条记录，以下是2011-2020 订阅

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A comparative study on machine learning algorithms for indoor positioning

A comparative study on machine learning algorithms for indoo...

引用

IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Sinem Bozkurt Gulin Elibol Serkan Gunal Ugur Yayan Dept. of Computer Engineering Dept. of Electrical & Electronics Engineering Eskisehir Osmangazi University Eskisehir Turkiye Dept. of Computer Engineering Anadolu University Eskisehir Turkiye R&D Department Inovasyon Muhendislik Ltd. Sti Eskisehir Turkiye

Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature, machine learning algorithms have widespread usage in estimating positions. One of the main problems in indoor positioning systems is to find out appropriate machine learning algorithm. In this paper, selected machine learning algorithms are compared in terms of positioning accuracy and computation time. In the experiments, UJIIndoorLoc indoor positioning database is used. Experimental results reveal that k-Nearest Neighbor (k-NN) algorithm is the most suitable one during the positioning. Additionally, ensemble algorithms such as AdaBoost and Bagging are applied to improve the decision tree classifier performance nearly same as k-NN that is resulted as the best classifier for indoor positioning.

关键词： Classification algorithms Floors Accuracy machine learning algorithms Training Decision trees

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Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

Comparative study of Principal Component Analysis based Intr...

引用

International Conference on Signal Processing, Communication and Networking (ICSCN)

作者： Krupa Joel Chabathula C.D. Jaidhar M.A. Ajay Kumara Department of IT NITK Surathkal Mangalore India

This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging to identify unknown attacks in promising time with no false positive and no false negative. Principal Component Analysis (PCA) curtails the amount of data to be compared by reducing their dimensions prior to classification that results in reduction of detection time. In this paper, PCA is adopted to reduce higher dimension dataset to lower dimension dataset. It is accomplished by converting network packet header fields into a vector then PCA applied over high dimensional dataset to reduce the dimension. The reduced dimension dataset is tested with Support Vector machines (SVM), K-Nearest Neighbors (KNN), J48 Tree algorithm, Random Forest Tree classification algorithm, Adaboost algorihm, Nearest Neighbors generalized Exemplars algorithm, Navebayes probabilistic classifier and Voting Features Interval classification algorithm. Obtained results demonstrates detection accuracy, computational efficiency with minimal false alarms, less system resources utilization. Experimental results are compared with respect to detection rate and detection time and found that TREE classification algorithms achieved superior results over other algorithms. The whole experiment is conducted by using KDD99 data set.

关键词： Principal component analysis machine learning algorithms Signal processing algorithms Mathematical model Accuracy Vegetation Support vector machines

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Scalable and parallel machine learning algorithms for statistical data mining - Practice & experience

Scalable and parallel machine learning algorithms for statis...

引用

Proceedings of the International Convention MIPRO

作者： M. Riedel M. Goetz M. Richerzhagen P. Glock C. Bodenstein A.S. Memon M.S. Memon Forschungszentrum Juelich Juelich Supercomputing Centre Juelich Germany University of Iceland School of Natural Sciences and Engineering Reykjavik Iceland

Many scientific datasets (e.g. earth sciences, medical sciences, etc.) increase with respect to their volume or in terms of their dimensions due to the ever increasing quality of measurement devices. This contribution will specifically focus on how these datasets can take advantage of new `big data' technologies and frameworks that often are based on parallelization methods. Lessons learned with medical and earth science data applications that require parallel clustering and classification techniques such as support vector machines (SVMs) and density-based spatial clustering of applications with noise (DBSCAN) are a substantial part of the contribution. In addition, selected experiences of related `big data' approaches and concrete mining techniques (e.g. dimensionality reduction, feature selection, and extraction methods) will be addressed too. In order to overcome identified challenges, we outline an architecture framework design that we implement with open available tools in order to enable scalable and parallel machine learning applications in distributed systems.

关键词： machine learning algorithms Standards Algorithm design and analysis Data mining Support vector machines Concrete Clustering algorithms

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Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms

Real time cyber attack analysis on Hadoop ecosystem using ma...

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Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)

作者： Md Tanzim Khorshed Neeraj Anand Sharma Aaron Vinek Dutt A.B.M. Shawkat Ali Yang Xiang School of Information Technology Deakin University Australia School of Science and Technology The University of Fiji Lautoka Western FJ School of Science and Technology The University of Fiji Fiji

Big Data technologies are exciting cutting-edge technologies that generate, collect, store and analyse tremendous amount of data. Like any other IT revolution, Big Data technologies also have big challenges that are obstructing it to be adopted by wider community or perhaps impeding to extract value from Big Data with pace and accuracy it is promising. In this paper we first offer an alternative view of "Big Data Cloud" with the main aim to make this complex technology easy to understand for new researchers and identify gaps efficiently. In our lab experiment, we have successfully implemented cyber-attacks on Apache Hadoop's management interface "Ambari". On our thought about "attackers only need one way in", we have attacked the Apache Hadoop's management interface, successfully turned down all communication between Ambari and Hadoop's ecosystem and collected performance data from Ambari Virtual machine (VM) and Big Data Cloud hypervisor. We have also detected these cyber-attacks with 94.0187% accurateness using modern machine learning algorithms. From the existing researchs, no one has ever attempted similar experimentation in detection of cyber-attacks on Hadoop using performance data.

关键词： Big data Cloud computing Classification algorithms Ecosystems machine learning algorithms Ports (Computers) Security

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HeteroSpark: A heterogeneous CPU/GPU Spark platform for machine learning algorithms

HeteroSpark: A heterogeneous CPU/GPU Spark platform for mach...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Peilong Li Yan Luo Ning Zhang Yu Cao Dept. of Electrical and Computer Engineering University of Massachusetts Lowell

ISBN: (纸本)9781467378925

Analytics algorithms on big data sets require tremendous computational capabilities. Spark is a recent development that addresses big data challenges with data and computation distribution and in-memory caching. However, as a CPU only framework, Spark cannot leverage GPUs and a growing set of GPU libraries to achieve better performance and energy efficiency. We present HeteroSpark, a GPU-accelerated heterogeneous architecture integrated with Spark, which combines the massive compute power of GPUs and scalability of CPUs and system memory resources for applications that are both data and compute intensive. We make the following contributions in this work: (1) we integrate the GPU accelerator into current Spark framework to further leverage data parallelism and achieve algorithm acceleration; (2) we provide a plug-n-play design by augmenting Spark platform so that current Spark applications can choose to enable/disable GPU acceleration; (3) application acceleration is transparent to developers, therefore existing Spark applications can be easily ported to this heterogeneous platform without code modifications. The evaluation of HeteroSpark demonstrates up to 18× speedup on a number of machine learning applications.

关键词： Graphics processing units Sparks Acceleration Libraries Computer architecture machine learning algorithms Big data

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Ranking of machine learning algorithms based on the performance in classifying DDoS attacks

Ranking of machine learning algorithms based on the performa...

引用

IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： R. R. Rejimol Robinson Ciza Thomas Terna Public Charitable Trust's College of Engineering Osmanabad Maharashtra IN College of Engineering Trivadrum India

Network Security has become one of the most important factors to consider as the Internet evolves. The most important attack which affects the availability of service is Distributed Denial of Service. The service disruption may cause substantial financial loss as well as damage to the concerned network system. The traffic patterns exhibited by the DDoS affected traffic can be effectively captured by machine learning algorithms. This paper gives an evaluation and ranking of some of the supervised machine learning algorithms with the aim of reducing type I and type II errors, increasing precision and recall while maintaining detection accuracy. The performance evaluation is done using Multi Criteria Decision Aid software called Visual PROMETHEE. This work demonstrates the effectiveness of ensemble based classifiers especially the ensemble algorithm of Adaboost with Random Forest as the base classifier. Publicly available datasets such as DARPA scenario specific dataset, CAIDA DDoS Attack 2007 and CAIDA Conficker are used to evaluate the algorithms.

关键词： Computer crime machine learning algorithms Feature extraction Classification algorithms Intrusion detection Internet

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Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection

Comparative analysis of machine learning algorithms along wi...

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International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM)

作者： Sumouli Choudhury Anirban Bhowal Department of Information Technology Indian Institute of Information Technology Allahabad India

Intrusion detection is one of the challenging problems encountered by the modern network security industry. A network has to be continuously monitored for detecting policy violation or suspicious traffic. So an intrusion detection system needs to be developed which can monitor network for any harmful activities and generate results to the management authority. Data mining can play a massive role in the development of a system which can detect network intrusion. Data mining is a technique through which important information can be extracted from huge data repositories. In order to spot intrusion, the traffic created in the network can be broadly categorized into following two categories- normal and anomalous. In our proposed paper, several classification techniques and machine learning algorithms have been considered to categorize the network traffic. Out of the classification techniques, we have found nine suitable classifiers like BayesNet, Logistic, IBK, J48, PART, JRip, Random Tree, Random Forest and REPTree. Out of the several machine learning algorithms, we have worked on Boosting, Bagging and Blending (Stacking) and compared their accuracies as well. The comparison of these algorithms has been performed using WEKA tool and listed below according to certain performance metrics. Simulation of these classification models has been performed using 10-fold cross validation. NSL-KDD based data set has been used for this simulation in WEKA.

关键词： Classification algorithms machine learning algorithms Logistics Accuracy Training Prediction algorithms Intrusion detection

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Accelerating common machine learning algorithms through GPGPU symbolic computing

Accelerating common machine learning algorithms through GPGP...

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Colombian Conference on Computing (CCC)

作者： Miguel C. Díaz Fabio A. González Raul Ramos-Pollan Universidad Nacional de Colombia Bogota CO

This paper evaluates the implementation of two well known machine learning algorithms, kernel k-means and logistic regression, using Graphics Processing Units (GPUs). The main goal was to do an implementation that exploited the processing power of GPU while keeping the implementation simple, easy to understand and modify. The paper presents an empirical analysis of the performance of the implementations under different execution scenarios.

关键词： Graphics processing units Kernel Logistics Clustering algorithms Libraries machine learning algorithms Algorithm design and analysis

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Predicting individual thermal comfort using machine learning algorithms

Predicting individual thermal comfort using machine learning...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Asma Ahmad Farhan Krishna Pattipati Bing Wang Peter Luh University of Connecticut Storrs CT US

Human thermal sensation in an environment may be delayed, which may lead to life threatening conditions, such as hypothermia and hyperthermia. This is especially true for senior citizens, as aging alters the thermal perception in humans. We envision a decision support system that predicts human thermal comfort in real-time using various environmental conditions as well psychological and physiological features, and suggest corresponding actions, which can significantly improve overall thermal comfort and health of individuals, especially senior citizens. The key to realize this vision is an accurate thermal comfort model. We propose a novel machine learning based approach to learn an individual's thermal comfort model. This approach identifies the best set of features, and then learns a classifier that takes a feature vector as input and outputs a corresponding thermal sensation class (i.e. “feeling cold”, “neutral” and “feeling warm”). Evaluation using a large-scale publicly available data demonstrates that when using Support Vector machines (SVM) classifiers, the accuracy of our approach is 76.7%, over two times higher than that of the widely adopted Fanger's model (which only achieves accuracy of 35.4%). In addition, our study indicates that two factors, a person's age and outdoor temperature that are not included in Fanger's model, play an important role in thermal comfort, which is a finding interesting in its own right.

关键词： Support vector machines Temperature sensors Accuracy machine learning algorithms Adaptation models

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Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

Systematic analysis of machine learning algorithms on EEG da...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alexander Chan Christopher E. Early Sishir Subedi Yuezhe Li Hong Lin Department of Statistics University of California Los Angeles CA Department of Science and Computer Engineering University of Houston - Clear Lake Houston TX Department of Computer Science and Engineering Technology University of Houston - Downtown Houston TX Department of Mathematics Illinois State University Normal IL

Electroencephalography (EEG) is a highly promising medium for brain-computer interfaces (BCI) with potentially extraordinary applications such as the direct control of prosthetics and exoskeletons. However, effective analysis and modeling of EEG data has been limited by its poor spatial resolution. EEG's low amplitude, brief and sporadic nature, compounded by extra-cranial noise, contribute to the difficulty of this problem. This systematic analysis provides strong evidence to guide future research in machine learning applied to real-time analysis of brain states using EEGs. The main goal of this research is to understand how the construction of data sets used in training models affects the accuracy of prominent machine learning algorithms, specifically: Random Forest, Boosting, Naïve Bayesian Classifier, k-Nearest Neighbors (KNN) and Support Vector machine (SVM). Herein, we present a systematic method (N = 153) to test the accuracy of prominent machine learning algorithms when varying three main components of the training data set: the permutations of subjects, the number of unique data sets used to generate the training data set, and the number of samples in each training data set. Our results strongly indicate that Random Forest consistently yields superior results when analyzing EEG data compared to other prominent machine learning algorithms. Furthermore, a pilot investigation was conducted on a mean-normalized feature for EEG data analysis. The pilot analysis (N = 28) confirmed Random Forest's analytical superiority in EEG data, shows signs of improved accuracy, and identifies a distinctive correlation between beta and delta waves and their respective active or idle brain states.

关键词： Brain models Systematics machine learning algorithms Electronic mail Electroencephalography Computational modeling

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