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Classification by bagged consistent itemset rules

Classification by bagged consistent itemset rules

引用

international conference on pattern recognition

作者： Yohji Shidara Mineichi Kudo Atsuyoshi Nakamura Graduate School of Information Science and Technology Hokkaido University Hokkaido Japan

Associative classifiers that utilize association rules have been widely studied. It has been shown that associative classifiers often outperform traditional classifiers. Associative classifiers usually find only rules with high support values, because reducing the minimum support to be satisfied increases computational cost. However, rules with low support but high confidence may contribute to classification. We have proposed an approach to build a classifier composed of almost all consistent (100% confident) rules. the proposed classifier was extended by introducing item reduction and bagging in order to relax the constraint of consistency, which resulted in slightly increased performance for 26 datasets from the UCI machine learning repository.

关键词： Itemsets Bagging Association rules Costs data mining machine learning Radiofrequency interference

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Incremental machine learning techniques for document layout understanding

Incremental machine learning techniques for document layout ...

引用

international conference on pattern recognition

作者： S. Ferilli M. Biba T.M.A. Basile F. Esposito Dipartimento di Informatica Università di Bari

ISBN: (纸本)9781424421749

In real-world digital libraries, artificial intelligence techniques are essential for tackling the automatic document processing task with sufficient flexibility. the great variability in document kind, content and shape requires powerful representation formalisms to catch all the domain complexity. the continuous flow of new documents requires adaptable techniques that can progressively adjust the acquired knowledge on documents as long as new evidence becomes available, even extending if needed the set of recognized document types. Both these issues have not yet been thoroughly studied. this paper presents an incremental first-order logic learning framework for automatically dealing with various kinds of evolution in digital repositories content: evolution in the definition of class definitions, evolution in the set of known classes and evolution by addition of new unknown classes. Experiments show that the approach can be applied to real-world.

关键词： machine learning Software libraries Artificial intelligence Shape data mining learning systems Automatic logic units Production systems Digital systems Technology management

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Novel intrusion prevention and detection methods

Novel intrusion prevention and detection methods

引用

international IEEE conference on Intelligent Systems, IS

作者： Vladimir S. Jotsov Institute for Information Technologies State University of Library Studies and Information Technologies Bulgarian Academy of Sciences Sofia Bulgaria

Analysis of contemporary information security systems (ISS) and especially the case of intrusion detection systems (IDS) shows us few character negative features and drawbacks. Original methods and combined anomaly and signature IDS applications are presented in the paper. Human-centered methods INCONSISTENCY, FUNNEL, CALEIDOSCOPE and CROSSWORD interact on a competitive principle and are controlled by a synthetic metamethod SMM. A research is going on for the possibilities of including other machine learning and data mining methods under the general control of SMM. their applications aim at computational discovery and knowledge acquisition. It is reinforced by identification and resolution of contradictions, self-learning and other methods for analysis of different types of models from the ISS domain. the complexity of application results is considered. the data analysis in the field frequently needs an act of creation especially if it is applied in a knowledge-poor environment. It is shown that even in this case the creative processes are based on applications of clear and well-formalized methods.

关键词： Intrusion detection data mining Information security Man machine systems Humans Artificial intelligence Intelligent systems machine learning Computer applications Knowledge acquisition

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Confidence rated boosting algorithm for generic object detection

Confidence rated boosting algorithm for generic object detec...

引用

international conference on pattern recognition

作者： Nayyar A. Zaidi David Suter Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

ISBN: (纸本)9781424421749

In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object detection. Confidence rated Ada-boost algorithm has not been applied to generic object detection problem; in that sense our work is novel. We represent images as bag of words, where the words are SIFT descriptors extracted over some interest points. We compare our boosting algorithm to another version of boosting algorithm called Gentle-boost. Our approach generalizes well and performs equal or better than Gentle-boost. We show our results on four categories from the Caltech data sets, in terms of ROC curves.

关键词： Boosting Object detection Object recognition machine learning Systems engineering and theory data mining Face recognition Shape Frequency Histograms

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Analysis of binary feature mapping rules for promoter recognition in imbalanced DNA sequence datasets using Support Vector machine

Analysis of binary feature mapping rules for promoter recogn...

引用

international IEEE conference on Intelligent Systems, IS

作者： Robertas Damasevicius Software Engineering Department Kaunas University of Technology Kaunas Lithuania

recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, i.e., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called support vector machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. the results of classification for drosophila and human sequence datasets are presented.

关键词： DNA Sequences Support vector machines Support vector machine classification Bioinformatics learning systems Encoding Biological cells Kernel Training data

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An Effective Algorithm for mining Quantitative Association Rules Based on High Dimension Cluster

An Effective Algorithm for Mining Quantitative Association R...

引用

international conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yunkai Guo Junrui Yang Yulei Huang

mining association rules plays an essential role in data mining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Although we can transform quantitative attributes into intervals and applying Boolean algorithms to the intervals. But this approach is not effective and is difficult to scale up for high-dimensional cases. An efficient algorithm, DBSMiner (density based sub-space miner), is proposed by using the notion of "density- connected" to cluster the high density sub-space of quantitative attributes and gravitation between grid / cluster to deal with the low density cells which may be missed by the previous algorithms, DBSMiner not only can solve the problems of previous approaches, but also can scale up well for high-dimensional cases. Evaluations on DBSMiner have been performed using the car and the shuttle databases maintained at the UCI machine learning repository. the results indicate that DBSMiner is effective and can scale up quite linearly with an increasing number of attributes.

关键词： Clustering algorithms data mining Association rules Partitioning algorithms Transaction databases Itemsets machine learning algorithms Remuneration Computer science Performance evaluation

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8. Advanced techniques for face-based biometrics

8. Advanced techniques for face-based biometrics

引用

international conference on pattern recognition

作者： Massimo Tistarelli Universita di Sassari

Face recognition is nowadays one of the most challenging biometric modalities for the identification of individuals. In the last two decades several experimental as well as commercial systems have been developed exploiting different physical properties of the face image. Either being based on processing 2D or 3D information all these methods perform a face classification of the individuals based on some relevant features extracted from the raw image data. the data acquisition, preprocessing and the feature extraction/selection are all topics of the greatest importance to design a good performing recognition system. At the same time, the right choice of the features to be used as the basis for the face representation, which must be based on the uniqueness of such features, as well as most advanced issues such as the incorporation of quality information and the cope for ageing effects, are all of paramount importance. the tutorial will consists of two sessions (half day of total duration) devoted to the description of both the basic and most advanced techniques related to face recognition. the lectures will provide a comprehensive outline of face- based biometrics, its relation to biological systems (the psychophysics of the human visual system), including the existing applications and commercial systems. the lectures will provide an in-depth analysis of the state-of-the-art algorithms for face-image analysis including: face detection and tracking, landmark localization, feature extraction, face representation and classification. the lectures will mainly explore the image processing aspects of the recognition process. As for classification, machine learning algorithms will be also presented, including kernel methods as related to learning and the approximation theory. the most relevant issues and problems will be raised, providing practical solutions and algorithms responding to them. Particular attention will be given to the most advanced and new techniques for face

关键词： Biometrics Face detection Face recognition Feature extraction machine learning algorithms Aging Algorithm design and analysis data mining data acquisition Biological systems

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Sparse Non-negative pattern learning for image representation

Sparse Non-negative Pattern Learning for image representatio...

引用

IEEE international conference on Image Processing

作者： Dian Gong Xuemei Zhao Qiong Yang Department of Electrical Engineering University of California Riverside CA USA Department of Electronics Engineering Tsinghua University Beijing China ESAT-PSI/VISICS Katholieke Universiteit Leuven Heverlee Belgium

In this paper, we propose sparse non-negative pattern learning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximation with sparseness constraints, and then features are extracted by the second part of the algorithm, a conjugate family based non-negative sparse feature extraction method. By combining sparse and non-negative constraints of patterns together, SNPL model gives a better representation for images than state-of-art methods. Beyond that, we give an analytical solution for feature extraction although it is approximate, and thereby we extract the features for self-taught learning framework in a faster and more stable way. We apply the new model to various areas, including pattern coding, feature extraction, and recognition. Experimental results show the advantages of SNPL model.

关键词： Image representation Feature extraction data mining Approximation algorithms Sparse matrices machine learning Principal component analysis Independent component analysis Testing pattern recognition

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Image classification: Are rule-based systems effective when classes are fixed and known?

Image classification: Are rule-based systems effective when ...

引用

international conference on pattern recognition

作者： Palaiahnakote Shivakumara Deepu Rajan Suresh A. Sadananthan School of Computing National University of Singapore Singapore School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781424421749

In this paper, we investigate if rule-based systems are useful for image classification problems when the number of classes is fixed. the rules are derived from simple edge features such as width and straightness. A class representative is calculated for each class according to the average percentage of edges that satisfy the rule for a particular class. this percentage for an unknown image is compared to the class representative to assign a label to it. the proposed system does not require extensive feature extraction and classification techniques. It is shown that the rule based system outperforms some of the reported results on scene classification.

关键词： Image classification Knowledge based systems Layout Image edge detection Image retrieval Feature extraction machine learning Road transportation data mining Information retrieval

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Embedding HMM’s-based models in a Euclidean space: the topological hidden Markov models

Embedding HMM’s-based models in a Euclidean space: The topo...

引用

international conference on pattern recognition

作者： Djamel Bouchaffra Grambling State University

ISBN: (纸本)9781424421749

One of the major limitations of HMM-based models is the inability to cope with topology: when applied to a visible observation (VO) sequence, HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named "topological hidden Markov models" (thMM's) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. We have applied the concept of thMM's to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map a protein primary structure to its 3D fold. the results show that the concept of second level thMM's outperforms the SHMM's and the SVM classifiers.

关键词： Hidden Markov models Shape Topology Speech recognition Predictive models machine learning data mining Signal analysis Proteins Support vector machines

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