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Intrusion detection in MANET using classification algorithms: The effects of cost and model selection

AD HOC NETWORKS 2013年第1期11卷 226-237页

作者： Mitrokotsa, Aikaterini Dimitrakakis, Christos Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Secur & Cryptog Lab LASEC CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Sch Comp & Commun Sci LIA CH-1015 Lausanne Switzerland

Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised classification algorithms for intrusion detection on a number of metrics. We measure their performance on a dataset, described in this paper, which includes varied traffic conditions and mobility patterns for multiple attacks. One of our goals is to investigate how classification performance depends on the problem cost matrix. Consequently, we examine how the use of uniform versusweighted cost matrices affects classifier performance. A second goal is to examine techniques for tuning classifiers when unknown attack subtypes are expected during testing. Frequently, when classifiers are tuned using cross-validation, data from the same types of attacks are available in all folds. This differs from real-world employment where unknown types of attacks may be present. Consequently, we develop a sequential cross-validation procedure so that not all types of attacks will necessarily be present across all folds, in the hope that this would make the tuning of classifiers more robust. Our results indicate that weighted cost matrices can be used effectively with most statistical classifiers and that sequential cross-validation can have a small, but significant effect for certain types of classifiers. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Intrusion detection classification algorithms Cost-sensitive classification Mobile Ad-hoc Networks (MANETs)

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Comparative analysis of a-priori and a-posteriori dietary patterns using state-of-the-art classification algorithms: A case/case-control study

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第3期59卷 175-183页

作者： Kastorini, Christina-Maria Papadakis, George Milionis, Haralampos J. Kalantzi, Kallirroi Puddu, Paolo-Emilio Nikolaou, Vassilios Vemmos, Konstantinos N. Goudevenos, John A. Panagiotakos, Demosthenes B. Univ Ioannina Sch Med GR-45110 Ioannina Greece Harokopio Univ Dept Nutr & Dietet Athens 17671 Greece Leibniz Univ Hannover Informat & Comp Sci Dept D-30167 Hannover Germany Univ Rome Dept Cardiovasc Resp Nephrol Anesthesiol & Geriat Lab Biotechnol Appl Cardiovasc Med I-00161 Rome Italy Hellen Red Cross Hosp Cardiol Clin Athens 11526 Greece Alexandra Hosp Dept Clin Therapeut Acute Stroke Unit Athens 11528 Greece

Objective: To compare the accuracy of a-priori and a-posteriori dietary patterns in the prediction of acute coronary syndrome (ACS) and ischemic stroke. This is actually the first study to employ state-of-the-art classification methods for this purpose. Methods and materials: During 2009-2010, 1000 participants were enrolled;250 consecutive patients with a first ACS and 250 controls (60 +/- 12 years, 83% males), as well as 250 consecutive patients with a first stroke and 250 controls (75 +/- 9 years, 56% males). The controls were population-based and age-sex matched to the patients. The a-priori dietary patterns were derived from the validated MedDietScore, whereas the a-posteriori ones were extracted from principal components analysis. Both approaches were modeled using six classification algorithms: multiple logistic regression (MLR), naive Bayes, decision trees, repeated incremental pruning to produce error reduction (RIPPER), artificial neural networks and support vector machines. The classification accuracy of the resulting models was evaluated using the C-statistic. Results: For the ACS prediction, the C-statistic varied from 0.587 (RIPPER) to 0.807 (MLR) for the a-priori analysis, while for the a-posteriori one, it fluctuated between 0.583 (RIPPER) and 0.827 (MLR). For the stroke prediction, the C-statistic varied from 0.637 (RIPPER) to 0.767 (MLR) for the a-priori analysis, and from 0.617 (decision tree) to 0.780 (MLR) for the a-posteriori. Conclusion: Both dietary pattern approaches achieved equivalent classification accuracy over most classification algorithms. The choice, therefore, depends on the application at hand. (C) 2013 Elsevier B.V. All rights reserved.

关键词： classification algorithms A-priori dietary patterns A-posteriori dietary patterns Coronary heart disease Stroke

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Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 86卷 95-106页

作者： Aktepe, Adnan Ersoz, Suleyman Toklu, Bilal Kirikkale Univ Fac Engn Dept Ind Engn TR-71450 Yahsihan Kirikkale Turkey Gazi Univ Fac Engn & Architecture Dept Ind Engn Ankara Turkey

Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： classification algorithms Customer satisfaction Customer loyalty Structural Equation Modeling WEKA LISREL

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An urgent precaution system to detect students at risk of substance abuse through classification algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第3期22卷 690-707页

作者： bulut, Faruk Bucak, Ihsan Omur Fatih Univ Fac Engn Dept Comp Engn Istanbul Turkey Meliksah Univ Fac Engn & Architecture Dept Elect & Elect Engn Kayseri Turkey

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing this type of addiction. The aim of this study is to detect a teenager's probability of being a drug abuser using classification algorithms in machine learning and data mining. The objective is not to test the classifiers theoretically on the benchmark datasets, but rather to use this study as a basis for advanced and detailed studies in this field in the future. This paper not only uses a special dataset but also focuses on psychometrics and statistics. The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.

关键词： Substance abuse risk assessment data mining machine learning classification algorithms

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Effective Indexes and classification algorithms for Supervised Link Prediction Approach to Anticipating Technology Convergence: A Comparative Study

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2023年第4期70卷 1430-1441页

作者： Hong, Suckwon Lee, Changyong Ulsan Natl Inst Sci & Technol Sch Business Adm Ulsan 44919 South Korea Sogang Univ Grad Sch Management Technol Seoul 04107 South Korea

This article conducts a comparative analysis to investigate the effects of different classification algorithms and structural proximity indexes on the performance of the supervised link prediction approach to anticipating technology convergence at different forecast horizons. For this, we identify relationships between technologies of interest for different time periods and compute 10 structural proximity indexes among unconnected technologies at each period. We develop a set of classification models that identify potential convergence among unconnected technologies where each model is configured differently by a classification algorithm and a combination of the proximity indexes. We compare the performance of the classification models to investigate effective combinations of classification algorithms and proximity indexes at different forecast horizons. The empirical analysis on Wikipedia articles about artificial intelligence technology indicates that random forest outperforms others in short-term forecasting while support vector machine outperforms others in mid-term forecasting. We also identify structural proximity indexes that produce higher performance when combined with the most effective algorithm at each forecast horizon. The results of this article are expected to offer guidelines for choosing classification algorithms and indexes when applying the supervised link prediction approach in anticipating technology convergence.

关键词： Indexes Internet Encyclopedias Electronic publishing Hypertext systems Convergence Patents classification algorithms comparative study proximity indexes supervised link prediction analysis technology convergence Wikipedia hyperlinks

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Comparison of different classification algorithms to identify geographic origins of olive oils

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JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2020年第4期57卷 1535-1543页

作者： Gumus, Ozgur Yasar, Erkan Gumus, Z. Pinar Ertas, Hasan Ege Univ Fac Engn Dept Comp Engn TR-35100 Izmir Turkey Ege Univ Cent Res Testing & Anal Lab Res & Applicat Ctr EG TR-35100 Izmir Turkey Ege Univ Fac Sci Dept Chem TR-35100 Izmir Turkey

Research on investigation and determination of geographic origins of olive oils is increased by consumers' demand to authenticated olive oils. classification algorithms which are machine learning methods can be employed for the authentication of olive oils. In this study, different classification algorithms were evaluated to reveal the most accurate one for authentication of Turkish olive oils. BayesNet, Naive Bayes, Multilayer Perception, IBK, Kstar, SMO, Random Forest, J48, LWL, Logistic Regression, Simple Logistic, LogitBoost algorithms were implemented on 61 chemical analysis parameters of 49 olive oil samples from 6 different locations at Western Turkey. These 61 parameters were obtained from five different chemical analyses which are stable carbon isotope ratio, trace elements, sterol compositions, FAMEs and TAGs. This study is the most comprehensive study to determine the geographical origin of Turkish olive oils in terms of these mentioned features. classification performances of the algorithms were compared using accuracy, specificity and sensitivity metrics. Random Forest, BayesNet, and LogitBoost algorithms were found as the best classification algorithms for authentication of Turkish olive oils. Using the classification model in this study, geographic origin of an unknown olive oil can be predicted with high accuracy. Besides, similar models can be developed to obtain useful information for authentication of other food products.

关键词： Machine learning classification algorithms Authentication Geographic origin Olive oil

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Effects of data set features on the performances of classification algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第5期40卷 1847-1857页

作者： Kwon, Ohbyung Sim, Jae Mun Kyung Hee Univ Sch Management Seoul 130701 South Korea Kyung Hee Univ Dept Int Management Seoul 130701 South Korea

As the need to analyze big data sets grows dramatically, the role that classification algorithms play in data mining techniques also increases. Big data analysis requires more of the data sets' characteristics to be included, such as data structure, variety of sources, and the rate of update frequency. In this paper, we evaluate scenarios that examine which data set characteristics most affect the classification algorithms' performance. It is still a complex issue to determine which algorithm is how strong or how weak in relation to which data set. Thus, our research experimentally examines how data set characteristics affect algorithm performance, both in terms of accuracy and in elapsed time. To do so, we use a multiple regression method to evaluate the causality between data set characteristics as independent variables, and performance metrics as dependent variables. We also examine the role that classification algorithms play as moderator in this causality. All benchmark data sets in a UCI database are used that are fit to run the classification algorithm. Based on the results of the experiment, we discuss the requirements of legacy classification algorithms to address big data analysis in a new business intelligence era. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Big data classification algorithms Performance evaluation Data mining

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Benchmarking classification algorithms for Radar-Based Human Activity Recognition

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2022年第12期37卷 37-40页

作者： Fioranelli, Francesco Zhu, Simin Roldan, Ignacio Delft Univ Technol Dept Microelect NL-2628 CD Delft Netherlands

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2-3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time-frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training-validation-testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article.

关键词： Radar Sensors classification algorithms Spectrogram Doppler radar Benchmark testing Radar imaging Radar applications radar data human activity recognition (HAR)

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GAUSSIAN MAXIMUM-LIKELIHOOD AND CONTEXTUAL classification algorithms FOR MULTICROP classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1987年第6期25卷 805-814页

作者： DIZENZO, S BERNSTEIN, R DEGLORIA, SD KOLSKY, HG IBM CORP PALO ALTO SCI CTRPALO ALTOCA 94304 CORNELL UNIV ITHACANY 14853

In this paper we review some of the ways in which context has been handled in the remote-sensing literature, and we introduce additional possibilities. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.

关键词： classification algorithms Remote sensing Maximum likelihood estimation Iterative algorithms Testing Statistics Degradation Fuzzy sets Fuzzy set theory Parallel processing

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Spatial and Quantitative Comparison of Topographically Derived Different classification algorithms Using AWiFS Data over Himalayas, India

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第12期46卷 1991-2002页

作者： Sood, Vishakha Gupta, Sheifali Gusain, Hemendra Singh Singh, Sartajvir Chitkara Univ Inst Engn & Technol Dept Elect & Commun Engn Patiala 140401 Punjab India DRDO Snow & Avalanche Study Estab Chandigarh 160017 India Chitkara Univ Sch Engn & Technol Dept Elect & Commun Engn Solan 174103 Himachal Prades India

In recent years, the significant increase in research on spatial information is observed. classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier;(b) k-mean clustering as unsupervised classifier;and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5-88.5%) as compared to MLC (81-84%) and k-mean (74-81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.

关键词： Topographic correction (TC) classification algorithms Subpixel classification Himalayas Advance Wide Field Sensor (AWiFS) satellite data

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