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Accurate estimates of the data complexity and success probability for various cryptanalyses

DESIGNS CODES AND CRYPTOGRAPHY 2011年第1-3期59卷 3-34页

作者： Blondeau, Celine Gerard, Benoit Tillich, Jean-Pierre INRIA Project Team SECRET Le Chesnay France

Many attacks on encryption schemes rely on statistical considerations using plaintext/ciphertext pairs to find some information on the key. We provide here simple formulae for estimating the data complexity and the success probability which can be applied to a lot of different scenarios (differential cryptanalysis, linear cryptanalysis, truncated differential cryptanalysis, etc.). Our work does not rely here on Gaussian approximation which is not valid in every setting but use instead a simple and general approximation of the binomial distribution and asymptotic expansions of the beta distribution.

关键词： Statistical cryptanalysis Success probability data complexity

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Revisiting Feature Selection with data complexity 20

Revisiting Feature Selection with Data Complexity

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20th IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Ngan Thi Dong Khosla, Megha Leibniz Univ Hannover L3S Res Ctr Hannover Germany

ISBN: (纸本)9781728195742

The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection (FS) methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal set of the most predictive features. However, the main question of which method to use when remains unanswered. We study the above problem from the perspective of data complexity and ask if data complexity measures can be used to guide the selection of the most-suitable method. We perform a comparative study of 11 feature selection methods over 27 publicly available datasets evaluated over a range of the number of selected features using classification as the downstream task. We (empirically) show that as regard to classification, the performance of all studied feature selection methods is highly correlated with the error rate of a nearest-neighbor based classifier. We also argue about the non-suitability of studied complexity measures to determine the optimal number of relevant features. While looking closely at several other aspects, we provide recommendations for choosing a particular FS method for a given dataset.

关键词： feature selection data complexity comparative study large scale correlation analysis

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A Comprehensive Scenario Agnostic data LifeCycle Model for an Efficient data complexity Management 12

A Comprehensive Scenario Agnostic Data LifeCycle Model for a...

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12th IEEE International Conference on e-Science (e-Science)

作者： Sinaeepourfard, Amir Garcia, Jordi Masip-Bruin, Xavier Marin-Tordera, Eva Univ Politecn Cataluna BarcelonaTech Adv Network Architectures Lab CRAAX Barcelona Spain

ISBN: (纸本)9781509042739

There is a vast amount of data being generated every day in the world, coming from a variety of sources, with different formats, quality levels, etc. This new data, together with the archived historical data, constitute the seed for future knowledge discovery and value generation in several fields of eScience. Discovering value from data is a complex computing process where data is the key resource, not only during its processing, but also during its entire life cycle. However, there is still a huge concern about how to organize and manage this data in all fields, and at all scales, for efficient usage and exploitation during all data life cycles. Although several specific data LifeCycle (DLC) models have been recently defined for particular scenarios, we argue that there is no global and comprehensive DLC framework to be widely used in different fields. For this reason, in this paper we present and describe a comprehensive scenario agnostic data LifeCycle (COSA-DLC) model successfully addressing all challenges included in the 6Vs, namely Value, Volume, Variety, Velocity, Variability and Veracity, not tailored to any specific environment, but easy to be adapted to fit the requirements of any particular field. We conclude that a comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data organization and integration, easing the adaptation to any kind of scenario, guaranteeing good quality data levels, and helping save design time and efforts for the research and industrial communities.

关键词： data LifeCycle data Management data complexity Vs Challenges

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An Extraction Method for the Characterization of the Fuzzy Rule Based Classification Systems' Behavior using data complexity Measures: A case of study with FH-GBML

An Extraction Method for the Characterization of the Fuzzy R...

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2010 IEEE World Congress on Computational Intelligence

作者： Luengo, Julian Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

ISBN: (纸本)9781424469208

When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to determine the domains of competence of Fuzzy Rule Based Classification Systems As a case of study we use the Fuzzy Hybrid Genetic Based Machine Learning method. We consider twelve metrics of data complexity in order to analyze the behavior patterns of this method, obtaining intervals of such data complexity measures with good or bad performance of it. Combining these intervals we obtain rules that describe both good or bad behaviors of the Fuzzy Rule Based Classification System mentioned. These rules allow describe both good or bad behaviors of the Fuzzy Rule Based Classification Systems mentioned, allowing us to characterize the response quality of the methods from the data set complexity metrics of a given data set. Thus, we can establish the domains of competence of the Fuzzy Rule Based Classification Systems considered, making it possible to establish when the method will perform well or badly prior to its application.

关键词： FH-GBML automatic extraction method behavior pattern computational complexity data complexity extraction method feature extraction fuzzy hybrid genetic based machine learning method fuzzy rule based classification system fuzzy set theory knowledge based systems pattern classification response quality

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DETECTION OF SHILLING ATTACK IN COLLABORATIVE FILTERING RECOMMENDER SYSTEM BY PCA AND data complexity 17

DETECTION OF SHILLING ATTACK IN COLLABORATIVE FILTERING RECO...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhang, Fei Deng, Zi-Jun He, Zhi-Min Lin, Xiao-Chuan Sun, Li-Li Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Foshan Univ Sch Elect & Informat Engn Foshan 528000 Peoples R China

ISBN: (纸本)9781538652145

Collaborative filtering (CF) recommender system has been widely used for its well performing in personalized recommendation, but CF recommender system is vulnerable to shilling attacks in which shilling attack profiles are injected into the system by attackers to affect recommendations. Design robust recommender system and propose attack detection methods are the main research direction to handle shilling attacks, among which unsupervised PCA is particularly effective in experiment, but if we have no information about the number of shilling attack profiles, the unsupervised PCA will be suffered. In this paper, a new unsupervised detection method which combine PCA and data complexity has been proposed to detect shilling attacks. In the proposed method, PCA is used to select suspected attack profiles, and data complexity is used to pick out the authentic profiles from suspected attack profiles. Compared with the traditional PCA, the proposed method could perform well and there is no need to determine the number of shilling attack profiles in advance.

关键词： Collaborative filtering Recommender system Shilling attack detection PCA data complexity

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On the Suitability of Combining Feature Selection and Resampling to Manage data complexity

On the Suitability of Combining Feature Selection and Resamp...

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13th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA 2009)

作者： Martin-Felez, Raul Mollineda, Ramon A. Univ Jaume 1 DLSI Inst New Imaging Technol Castellon de La Plana Spain

ISBN: (纸本)9783642142635

The effectiveness of a learning task depends on data complexity (class overlap, class imbalance, irrelevant features, etc.). When more than one complexity factor appears, two or more preprocessing techniques should be applied. Nevertheless, no much effort has been devoted to investigate the importance of the order in which they can be used. This paper focuses on the joint use of feature reduction and balancing techniques, and studies which could be the application order that leads to the best classification results. This analysis was made on a specific problem whose aim was to identify the melodic track given a MIDI file. Several experiments were performed from different imbalanced 38-dimensional training sets with many more accompaniment tracks than melodic tracks, and where features were aggregated without any correlation study. Results showed that the most effective combination was the ordered use of resampling and feature reduction techniques.

关键词： data complexity feature reduction class imbalance problem melody finding music information retrieval

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Random Projection Through the Lens of data complexity Indicators 21

Random Projection Through the Lens of Data Complexity Indica...

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21st IEEE International Conference on data Mining (IEEE ICDM)

作者： Thummanusarn, Yamonporn Kahan, Ata Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (纸本)9781665424271

Random projection (RP) is a simple and efficient tool for dimensionality reduction and has been applied in many machine learning algorithms. Theoretical guarantees ensure that the method approximately preserves the pairwise Euclidean distances with high probability. However, worst-case theoretical bounds on the required target dimension of the projection are not sufficiently tight for direct practical use, so for a large data set most often much lower values are being used in practice. While theory also suggests that this may be due to the low complexity structure of the underlying data support, this structure is typically unknown in practice. Here we investigate the use of a previously proposed battery of data complexity indicators (DCIs) to gain insight into how random projection changes the data complexity for classification problems. Our experimental results show that complexity increases with compression, but in most cases we can project data onto fairly low dimensions while maintaining nearly the same level of data complexity as that of the original data. This may offer some guidance for the choice of the target dimension in practice, although it should be interpreted with caution.

关键词： Classification data complexity Random Projection

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Incremental Learning Algorithm of data complexity Based on KNN Classifier

Incremental Learning Algorithm of Data Complexity Based on K...

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International Symposium on Community-Centric Systems (CcS)

作者： Li Jie Xue Yaxu Yu Yadong Pingdingshan Univ Coll Elect & Mech Engn Pingdingshan Peoples R China

ISBN: (数字)9781728187419

ISBN: (纸本)9781728187419

In practice, new data are constantly generated, and the existing data complexity algorithms are based on the idea of batch learning. In the face of the dynamic increase of data scale, how to measure the characteristic information of data has become an urgent problem to be solved in the field of data mining. This paper focuses on this problem and further studies its incremental learning function on the basis of in-depth discussion of data complexity proposed by TK Ho et al. Among them, N3 and N4 are the complexity indexes based on KNN classifier (K=1). In this paper, the incremental learning algorithm I1NN was proposed on the basis of 1-NN classifier, and its feasibility and validity were verified on both the artificial data set and the UCI public data set.

关键词： data complexity Incremental learning KNN

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On the Dimensions of data complexity through Synthetic data Sets

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11th International Conference of the Catalan-Association-for-Artificial-Intelligence

作者： Macia, Nuria Bernado-Mansilla, Ester Orriols-Puig, Albert Univ Ramon Llull Grp Recerca Sistemes Intel Ligents Engn & Arquite Barcelona 08022 Spain

ISBN: (纸本)9781586039257

This paper deals with the characterization of data complexity and the relationship with the classification accuracy. We study three dimensions of data complexity: the length of the class boundary, the number of features, and the number of instances of the data set. We find that the length of the class boundary is the most relevant dimension of complexity, since it can be used as an estimate of the maximum achievable accuracy rate of a classifier. The number of attributes and the number of instances do not affect classifier accuracy by themselves, if the boundary length is kept constant. The study emphasizes the use of measures revealing the intrinsic structure of data and recommends their use to extract conclusions on classifier behavior and their relative performance in multiple comparison experiments.

关键词： data complexity Classification Dimensionality Synthetic data sets

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Analyzing data complexity Using Metafeatures for Classification Algorithm Selection

Analyzing Data Complexity Using Metafeatures for Classificat...

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Shah, Rushit N. Khemani, Varun Azarian, Michael H. Pecht, Michael G. Su, Yan Univ Maryland CALCE College Pk MD 20740 USA Nanjing Univ Aeronaut & Astronaut Civil Aviat Coll Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9781538653807

Supervised machine learning is defined as the task of seeking algorithms which, based on reasoning gained from historical data, produce a general hypothesis, can then be used to infer knowledge about new data in the future. Classification, an instance of supervised learning, is statistically defined as the problem of identifying to which particular subpopulation a new observation of data belongs. Given the large number of available classification algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the best method to analyze a new data set has become an ever more challenging task. Typically, finding the best classifier for a given data set involves empirically iterating through all candidate classifiers and choosing the one which provides the best classification accuracies. Clearly, this task is computationally very expensive and the computation cost increases with the addition of each new candidate algorithm. This problem is compounded by the fact that there is not adequate generalizability of these classification methods over data sets from different domains. For example, classifier performance obtained with medical imaging data may not hold for financial data when trying to achieve a similar classification task using the same classifier. How, then, does one efficiently choose a classifier which will provide better classification performance than others? In this context, this study aims to streamline the task of algorithm selection for classification using the meta-learning framework. We propose a methodology to analyze empirically a set of measures of data complexity, known as metafeatures, and investigate their influence on the classification performance of several widely used classifiers. Doing so allows a map of a performance metric to be generated over the metafeature space of data sets. This map is partitioned into regions where some classifiers perform better than others. Once implemented, a new data set can be located in

关键词： metafeatures algorithm selection data complexity

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