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9th Computer Science and Electronic Engineering (CEEC)

作者： Hao, Cong Yoshimura, Takeshi Waseda Univ Grad Sch IPS Tokyo Japan

ISBN: (纸本)9781538630075

The study on machine learning has been flourishing for several years, and machine learning algorithms are being applied to various fields with great achievements. In this paper, combining the on-line machine learning method into optimization algorithms is to be studied. In many heuristic optimization algorithms, one common way to reduce execution time and improve solution optimality is, first estimating the quality of a set of candidate solutions, and solving only promising candidates in detail. Currently most estimations are performed by empirical equations, whose accuracy greatly relies on the how well the equation is designed. In this paper, we propose an on-line learning based estimator to perform the solution estimation in heuristic algorithms to improve estimation accuracy. Then a simple case study is discussed, where a local search based heuristic with random start is used, and an on-line estimator considering the properties of local search is proposed. The experiments show that the accuracy of on-line estimator is much higher than the static estimator, and is also higher than a general off-line pretrained learner. Even though the on-line estimator introduced special time for its training, the heuristic algorithm still speeds up by 3.7X without optimality sacrifice.

关键词： Optimization machine learning algorithms Estimation Algorithm design and analysis Heuristic algorithms Training Clustering algorithms

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Improving the matching of registered unemployed to job offers through machine learning algorithms

Improving the matching of registered unemployed to job offer...

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作者： Cruz, Paula Isabel Moura Meireles Universidade Nova de Lisboa

学位级别：硕士

Due to the existence of a double-sided asymmetric information problem on the labour marketcharacterized by a mutual lack of trust by employers and unemployed people, not enough job matchesare facilitated by public employment services (PES), which seem to be caught in a low-end equilibrium. In order to act as a reliable third party, PES need to build a good and solid reputation among their mainclients by offering better and less time consuming pre-selection services. The use of machine-learning, data-driven relevancy algorithms that calculate the viability of a specific candidate for a particular jobopening is becoming increasingly popular in this field. Based on the Portuguese PES databases (CVs, vacancies, pre-selection and matching results), complemented by relevant external data published byStatistics Portugal and the European Classification of Skills/Competences, Qualifications andOccupations (ESCO), the current thesis evaluates the potential application of models such as RandomForests, Gradient Boosting, Support Vector machines, Neural Networks Ensembles and other tree-basedensembles to the job matching activities that are carried out by the Portuguese PES, in order tounderstand the extent to which the latter can be improved through the adoption of automatedprocesses. The obtained results seem promising and point to the possible use of robust algorithms suchas Random Forests within the pre-selection of suitable candidates, due to their advantages at variouslevels, namely in terms of accuracy, capacity to handle large datasets with thousands of variables, including badly unbalanced ones, as well as extensive missing values and many-valued categoricalvariables.

关键词： Job Matching Registered Unemployed with PES machine learning algorithms Social Data ESCO

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machine learning algorithms for Environmental Sound Recognition: Towards Soundscape Semantics 15

Machine Learning Algorithms for Environmental Sound Recognit...

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Audio Mostly (AM) Conference on Sound, Semantics and Social Interaction

作者： Bountourakis, Vasileios Vrysis, Lazaros Papanikolaou, George Aristotle Univ Thessaloniki AUTh Univ Campus Thessaloniki 54124 Greece

ISBN: (纸本)9781450338967

This paper investigates methods aiming at the automatic recognition and classification of discrete environmental sounds, for the purpose of subsequently applying these methods to the recognition of soundscapes. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively little research has been done towards recognizing non-speech environmental sounds. For this reason, in this paper, we apply existing techniques that have been proved efficient in the other two domains. These techniques are comprehensively compared to determine the most appropriate one for addressing the problem of environmental sound recognition.

关键词： Environmental Sound Recognition audio classification semantic audio analysis computer audition feature extraction feature selection machine learning algorithms

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A Comparison Study of machine learning Based algorithms for Fatigue Crack Growth Calculation

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MATERIALS 2017年第5期10卷 543-543页

作者： Wang, Hongxun Zhang, Weifang Sun, Fuqiang Zhang, Wei Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The relationships between the fatigue crack growth rate (da/dN) and stress intensity factor range (Delta K) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model (K* approach). The results show that the predictions of MLAs are superior to those of K* approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

关键词： fatigue crack growth machine learning algorithms fatigue life prediction extreme learning machine (ELM) stress ratio

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Classification of SSH Attacks Using machine learning algorithms

Classification of SSH Attacks Using Machine Learning Algorit...

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International Conference on IT Convergence and Security (ICITCS)

作者： Gokul Kannan Sadasivam Chittaranjan Hota Bhojan Anand Department of Computer Science and Information Systems BITS Hyderabad Telangana India School of Computing National University of Singapore Singapore

ISBN: (纸本)9781509037667

SSH Attacks are of various types: SSH port scanning, SSH Brute-force attacks, Attacks using compromised SSH server. Attacks using a compromised server could be DoS attacks, Phishing attacks, E- mail spamming and so on. This paper questions whether the attacks from a compromised SSH server be segregated from other attacks using the network flows. In this work, we categorize SSH attacks into two types. The first category consists of all attack activities after a successful compromise of an SSH server. We name it as "severe" attacks. The second type includes all attacks leading to a successful compromise. It consists of SSH port scanning, SSH Brute-force attack, and compromised SSH server with no activities. The second category is named as "not-so-severe" attacks. We employ machine learning algorithms, namely, Naive Bayes learner, Logistic Regression, J48 decision tree, and Support Vector machine to classify these attacks. Suitable features were selected based on domain knowledge, literature survey, and feature selection technique to evaluate the performance of machine learning algorithms using the metrics accuracy, sensitivity, precision, and F-score.

关键词： machine learning algorithms Servers Ports (Computers) Protocols Feature extraction Decision trees Support vector machines

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Scalable machine-learning algorithms for big data analytics: a comprehensive review

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WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2016年第6期6卷 194-214页

作者： Gupta, Preeti Sharma, Arun Jindal, Rajni IGDTUW Dept IT New Delhi India DTU Dept CSE Delhi India

Big data analytics is one of the emerging technologies as it promises to provide better insights from huge and heterogeneous data. Big data analytics involves selecting the suitable big data storage and computational framework augmented by scalable machine-learning algorithms. Despite the tremendous buzz around big data analytics and its advantages, an extensive literature survey focused on parallel data-intensive machine-learning algorithms for big data has not been conducted so far. The present paper provides a comprehensive overview of various machine-learning algorithms used in big data analytics. The present work is an attempt to identify the gaps in the work already performed by researchers, thus paving the way for further quality research in parallel scalable algorithms for big data. (C) 2016 John Wiley & Sons, Ltd

关键词： machine learning algorithms Scalability Data analysis literature survey big data

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A comparison of different machine learning algorithms for automatic classification of sonar targets

A comparison of different machine learning algorithms for au...

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OCEANS

作者： Henrik Berg Karl Thomas Hjelmervik Dan Henrik Sekse Stender Tale Solberg Såstad Norwegian Defence Research Establishment (FFI) Norway

A well-known problem with modern anti-submarine warfare sonars with narrow beamwidths and wide frequency bandwidths, is the frequent occurence of false alarms, particularly in littoral environments. This increases the workload of sonar operators and also reduces the usefulness of automatic systems such as autonomous underwater vehicles, since their limited communication abilities hinder them from sharing large amounts of contacts. In this paper, four traditional machine learning algorithms are tested on sonar data with a high amount of false alarms together with synthetic submarine echoes. It is shown that some of the algorithms can outperform simple signal to noise ratio (SNR) thresholding by a significant amount, but that the performance is highly dependent on the parameter values chosen for each algorithm. These parameters are therefore investigated in order to determine their relative significance.

关键词： Underwater vehicles machine learning algorithms Sonar Signal to noise ratio Bagging Training Feature extraction

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Comparative analysis of machine learning algorithms in OCR

Comparative analysis of machine learning algorithms in OCR

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Vanita Jain Arun Dubey Amit Gupta Sanchit Sharma Dept of IT Bharati Vidyapeeth's College of Engineering New Delhi India

ISBN: (纸本)9781467394178

The purpose of this research is to implement different machine learning algorithms in optical character recognition. The algorithms used the pixel density of image of handwritten digits as an input. The algorithms when implemented produced the value of labels of each handwritten digit. The value of labels generated, was then matched with the actual value of labels of the MNIST handwritten digits to determine the accuracy of an algorithm. machine learning algorithms that have been used for this research are Naïve Bayes, Naïve Bayes with Laplace Smoothing, Sequential Minimal Optimization, C4.5 decision trees and Logistic Regression. The accuracy for each of the algorithm was calculated and Logistic regression was found out to be the most accurate of them all for handwritten digits.

关键词： Logistics Algorithm design and analysis Classification algorithms machine learning algorithms Optical character recognition software Training Decision trees

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Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data (vol 42, pg 2928, 2015)

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第19期42卷 6806-6806页

作者： Park, Byeonghwa Bae, Jae Kwon Hannam Univ Dept Business Stat Taejon 306791 South Korea Keimyung Univ Dept Management Informat Syst Daegu 704701 South Korea

House sales are determined based on the Standard & Poor’s Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naïve Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction.

关键词： Housing price index Housing price prediction model machine learning algorithms C4.5 RIPPER Naï ve Bayesian AdaBoost

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A Python Framework for Exhaustive machine learning algorithms and Features Evaluations

A Python Framework for Exhaustive Machine Learning Algorithm...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Fabien Dubosson Stefano Bromuri Michael Schumacher AISLab HES-SO Valais//Wallis Management Science and Technology Open University Netherlands

ISBN: (纸本)9781509018598

machine learning domain has grown quickly the last few years, in particular in the mobile eHealth domain. In the context of the DINAMO project, we aimed to detect hypoglycemia on Type 1 diabetes patients by using their ECG, recorded with a sport-like chest belt. In order to know if the data contain enough information for this classification task, we needed to apply and evaluate machine learning algorithms on several kinds of features. We have built a Python toolbox for this reason. It is built on top of the scikit-learn toolbox and it allows evaluating a defined set of machine learning algorithms on a defined set of features extractors, taking care of applying good machine learning techniques such as cross-validation or parameters grid-search. The resulting framework can be used as a first analysis toolbox to investigate the potential of the data. It can also be used to fine-tune parameters of machine learning algorithms or parameters of features extractors. In this paper we explain the motivation of such a framework, we present its structure and we show a case study presenting negative results that we could quickly spot using our toolbox.

关键词： Feature extraction machine learning algorithms Biomedical monitoring Electrocardiography Diabetes Data mining

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