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Quantification of side-channel information leaks based on data complexity measures for web browsing

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2015年第4期6卷 607-619页

作者： He, Zhi-Min Chan, Patrick P. K. Yeung, Daniel S. Pedrycz, Witold Ng, Wing W. Y. S China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G7 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Website fingerprinting attack can identify the visited websites by analyzing the side-channel information of the network traffic even though it is transferred through an encrypted tunnel. The security of web browsing can be evaluated by quantifying the side-channel information leaks. However, most of the current leak quantification measures focus on web applications and may be impractical in web browsing due to their time complexity. Although the revised models were proposed to simplify computations, their assumptions may not be suitable for web browsing. In this paper, the problem of website fingerprinting is analyzed from the viewpoint of pattern classification. The data complexity measures, which quantify the difficulty of separating classes in a classification problem, are applied to describe the leak quantification. The performance of these data complexity measures in representing information leaks is discussed and compared with the existing approaches. This comparative analysis is realized conceptually and through experiments by using two website fingerprinting countermeasures: traffic morphing and BuFLO. Moreover, the parameter selection model based on the leak quantification is proposed to estimate suitable parameters for the website fingerprinting countermeasure. The experimental results confirm that the countermeasures with parameters selected according to the data complexity measures are more secure than other leak quantification measures.

关键词： data complexity measures Side-channel leak quantification Web browsing Traffic analysis countermeasure

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Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

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PATTERN RECOGNITION 2013年第1期46卷 355-364页

作者： Saez, Jose A. Luengo, Julian Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence CITIC UGR E-18071 Granada Spain Univ Burgos LSI Dept Civil Engn Burgos 09006 Spain

Classifier performance, particularly of instance-based learners such as k-nearest neighbors, is affected by the presence of noisy data. Noise filters are traditionally employed to remove these corrupted data and improve the classification performance. However, their efficacy depends on the properties of the data, which can be analyzed by what are known as data complexity measures. This paper studies the relation between the complexity metrics of a dataset and the efficacy of several noise filters to improve the performance of the nearest neighbor classifier. A methodology is proposed to extract a rule set based on data complexity measures that enables one to predict in advance whether the use of noise filters will be statistically profitable. The results obtained show that noise filtering efficacy is to a great extent dependent on the characteristics of the data analyzed by the measures. The validation process carried out shows that the final rule set provided is fairly accurate in predicting the efficacy of noise filters before their application and it produces an improvement with respect to the indiscriminate usage of noise filters. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Classification Noisy data Noise filtering data complexity measures Nearest neighbor

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Centralized vs. distributed feature selection methods based on data complexity measures

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KNOWLEDGE-BASED SYSTEMS 2017年 117卷 27-45页

作者： Moran-Fernandez, L. Bolon-Canedo, V. Alonso-Betanzos, A. Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

In the era of Big data, many datasets have a common characteristic, the large number of features. As a result, selecting the relevant features and ignoring the irrelevant and redundant features has become indispensable. However, when dealing with large amounts of data, Most existing feature selection algorithms do not scale well, and their efficiency may significantly deteriorate to the point of becoming inapplicable. Moreover, data is often distributed in multiple locations, and it is not economic or legal to gather it in a single site. For these reasons, we propose a distributed approach for partitioned data using two techniques: horizontal (i.e. by samples) and vertical (i.e. by features). Unlike than existing procedures to combine the partial outputs obtained from each partition of data, we propose a merging process using the theoretical complexity of these feature subsets. The novel procedure tested in 11 datasets has proved to be useful, showing competitive results both in terms of runtime and classification accuracy. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Distributed learning Feature selection data complexity measures Classification

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A Meta-Fuzzy Classifier for Specifying Appropriate Fuzzy Partitions by Genetic Fuzzy Rule Selection with data complexity measures

A Meta-Fuzzy Classifier for Specifying Appropriate Fuzzy Par...

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IEEE International Conference on Fuzzy Systems (FUZZ 2011)

作者： Nojima, Yusuke Nishikawa, Shinya Ishibuchi, Hisao Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Naka Ku Osaka 5998531 Japan

ISBN: (纸本)9781424473175

Tens of thousands of classifiers have been proposed so far. There is no best classifier among them for all the existing data sets. The performance of each classifier often depends on the data sets used for comparison. Even for a single classifier, suitable parameters of the classifier also depend on the data sets. That is, there is a possibility that a suited classifier and its parameter specification can be chosen beforehand if the target data sets or their characteristics were known. In recent years, a number of data complexity measures have been proposed to characterize data sets. The aim of this study is to develop a meta-classifier for selecting an appropriate classifier and/or its appropriate parameter specification by means of data complexity measures. In this paper, we focus on the parameter specification of fuzzy classifiers using data complexity measures as a preliminary study. To construct a meta-classifier, we generate a large number of artificial data sets from Keel benchmark data sets. Then we generate meta-patterns which are composed of the values of data complexity measures as inputs and an appropriate fuzzy partition as an output. Using meta-patterns, a meta-classifier is designed by multiobjective genetic fuzzy rule selection. We evaluate the proposed method through leave one-group out cross-validation.

关键词： fuzzy classifier design data complexity measures meta-learning pattern classification knowledge acquisition

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data complexity based hybrid approach to select a model for wind speed forecasting

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 7198-7215页

作者： Kushwah, Anil Kumar Wadhvani, Rajesh Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal India

Recent wind speed forecasting works have applied various time series decomposition methods to segregate a complex wind time series into linear and nonlinear components. Then, existing statistical techniques are used to model each component separately. This approach is reliable and produces accurate results when applied to complex wind time series. However, forecasting the stationary wind time series may produce unsatisfactory results. In order to address this problem, we propose a hybrid approach for wind speed forecasting. In this approach, seven different data complexity measures are used to calculate time series data complexity. Then, the measured complexity is used by the proposed model to decide whether to go with the decomposition technique or not. This paper has suggested Empirical Mode Decomposition for decomposition, which has achieved an incredible surge in a large number of data streams. Similarly, two leading statistical forecasting approaches, i.e., Autoregressive Integrated Moving Average and Generalized Autoregressive Score, are employed to model linear time series and decomposed components of the complex nonlinear time series. For comparative analysis, Mean Absolute Error, Root Mean Squared Error, and R-square are used as evaluation metrics. Experimental result in this work suggests that the proposed hybrid approach performs equivalently well for complex time series and achieves considerable improvement for stationary wind time series. The percentage improvement is 95.29% in the case of ARIMA and 49.09% when GAS is applied.

关键词： Wind speed forecasting ARIMA GAS empirical mode decomposition data complexity measures hybrid model

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Can classification performance be predicted by complexity measures? A study using microarray data

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期51卷 1067-1090页

作者： Moran-Fernandez, L. Bolon-Canedo, V. Alonso-Betanzos, A. Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

data complexity analysis enables an understanding of whether classification performance could be affected, not by algorithm limitations, but by intrinsic data characteristics. Microarray datasets based on high numbers of gene expressions combined with small sample sizes represent a particular challenge for machine learning researchers. This type of data also has other particularities that may negatively affect the generalization capacity of classifiers, such as overlaps between classes and class imbalance. Making use of several complexity measures, we analyzed the intrinsic complexity of several microarray datasets with and without feature selection and then explored the connection with the empirical results obtained by four widely used classifiers. Experimental results for 21 binary and multiclass datasets demonstrate that a correlation exists between microarray data complexity and the classification error rates.

关键词： data complexity measures Classification Microarray data Feature selection Filters

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Exploring diversity in data complexity and classifier decision spaces for pool generation

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INFORMATION FUSION 2023年 89卷 567-587页

作者： Monteiro Jr, Marcos Britto Jr, Alceu S. Barddal, Jean P. Oliveira, Luiz S. Sabourin, Robert Pontificia Univ Catolica Parana PUCPR Curitiba PR Brazil State Univ Ponta Grossa UEPG Ponta Grossa PR Brazil Fed Univ Parana UFPR Curitiba PR Brazil Univ Quebec Ecole Technol Super ETS Montreal PQ Canada

This paper introduces a novel method for classifier pool generation in which a two-level strategy explores diversity in both data complexity and classifier decision spaces. The rationale is to induce pool members using data subsets representing subproblems with different difficulties while promoting diversity in classifiers' decisions. Two possible variants of the proposed method with a focus on maximum dispersion and maximum accuracy are presented. These differ in the property used to define the best pool of classifiers provided by an optimization process. A robust experimental protocol encompassing 28 classification datasets shows that the proposed pool generation provided the best accuracy on 327 over 336 experiments (97.3%) when compared to well-known pool generation methods to provide multiple classifier systems with and without dynamic selection.

关键词： Classifier pool generation Diversity data complexity measures

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A set of measures designed to identify overlapped instances in software defect prediction

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COMPUTING 2017年第9期99卷 889-914页

作者： Gupta, Shivani Gupta, Atul Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur India

The performance of the learning models will intensely rely on the characteristics of the training data. The previous outcomes recommend that the overlapping between classes and the presence of noise have the most grounded impact on the performance of learning algorithm, and software defect datasets are no exceptions. The class overlap problem is concerned with the performance of machine learning classifiers critical problem is class overlap in which data samples appear as valid examples of more than one class which may be responsible for the presence of noise in datasets. We aim to investigate how the presence of overlapped instances in a dataset influences the classifier's performance, and how to deal with class overlapping problem. To have a close estimate of class overlapping, we have proposed four different measures namely, nearest enemy ratio, subconcept ratio, likelihood ratio and soft margin ratio. We performed our investigations using 327 binary defect classification datasets obtained from 54 software projects, where we first identified overlapped datasets using three data complexity measures proposed in the literature. We also include treatment effort into the prediction process. Subsequently, we used our proposed measures to find overlapped instances in the identified overlapped datasets. Our results indicated that by training a classifier on a training data free from overlapped instances led to an improved classifier performance on the test data containing overlapped instances. The classifiers perform significantly better when the evaluation measure takes the effort into account.

关键词： data complexity measures Class overlapping data mining Machine learning Software defect prediction

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Simulated annealing based undersampling (SAUS): a hybrid multi-objective optimization method to tackle class imbalance

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APPLIED INTELLIGENCE 2022年第2期52卷 2092-2110页

作者： Chennuru, Venkata Krishnaveni Timmappareddy, Sobha Rani Univ Hyderabad SCIS Hyderabad India

Learning from imbalanced datasets is a challenging problem in machine learning research since the performance of the traditional classifiers suffer from biased classification towards the Majority class resulting in a low Minority class prediction rate. The inherent assumptions of equal class distribution and accuracy-driven evaluation are the identified reasons behind this degraded performance. Further, false negatives have higher penalty than the false positives. A simple logical solution to mitigate this issue is to construct a balanced training set from the imbalanced one. However, several such sets of balanced training sets can be formed for a given imbalanced set from which an optimal balanced training set has to be obtained. This is a computationally intractable problem and prone to local-optimal maxima/minima. To address these issues, a Simulated Annealing-based Under Sampling (SAUS) method is proposed. Simulated annealing is a popular meta-heuristic search algorithm, which implements a novel cost function in terms of Balanced Error Rate. This cost function strikes a balance between Sensitivity and Specificity measures while evaluating the solution at each iteration in the subsampling process and also is free from the local trap. The experimental results of SAUS demonstrate that the average Sensitivity measure on the test set has improved from 0.68 to 0.86 and proves its efficacy in tackling the imbalance issue in the dataset. Area Under the ROC Curve (AUC) results also demonstrate that SAUS outperforms several popular undersampling methods. SAUS works on par with state-of-the-art solutions for the class imbalance problem.

关键词： Class imbalance sensitivity Balanced error rate Imbalanced datasets data complexity measures Meta-heuristic Simulated annealing

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Using complexity measures to determine the structure of directed acyclic graphs in multiclass classification

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APPLIED SOFT COMPUTING 2018年 65卷 428-442页

作者： Quiterio, Thaise M. Lorena, Ana C. Univ Fed Sao Paulo UNIFESP Inst Ciencia & Tecnol Machine Learning Res Grp Av Cesare Mansueto Giulio Lattes 1201 Sao Jose Dos Campos SP Brazil

There are various strategies for decomposing a multiclass classification problem into multiple binary sub-problems. One of the most employed strategies is one-versus-one (OVO), in which there is one binary classifier for each pair of the classes of the original problem. Directed acyclic graphs (DAGs) can be used to combine the outputs of the OVO classifiers in a multiclass prediction. Although the use of these graphs presents some advantages, like reducing the number of classifiers consulted at each prediction, the number of possible graph structures for a given problem is factorial on its number of classes. This paper presents some data-driven strategies designed to find DAG structures suited for each multiclass classification problem. They are guided by the values of some data complexity measures, which evaluate the difficulty of the binary sub-problems. The idea is to place simpler binary sub-problems at the top of the DAG hierarchies. Experimentally, a small variation between the predictive results of distinct DAG structures was found, even for problems with a large number of classes. For this reason, simple greedy algorithms are in several cases able to obtain suitable DAG structures when compared to a more sophisticated genetic algorithm search strategy. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Machine learning Decomposition strategies Multiclass classification Directed acyclic graphs data complexity measures

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