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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Parallel classification system based on an ensemble of mixture of experts

Parallel classification system based on an ensemble of mixtu...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Moreno-Montiel, Benjamín MacKinney-Romero, René Departamento de Ingeniería Eléctrica Universidad Autonóma Metropolitana-Iztapalapa Av. San Rafael Atlixco No. 186 Iztapalapa Distrito Federal Mexico

ISBN: (纸本)9789897580185

The classification of large amounts of data is a challenging problem that only a small number of classification algorithms can handle. In this paper we propose a Parallel Classification System based on an Ensemble of Mixture of Experts (PCEM). The system uses MIMD (Multiple Instruction and Multiple data Stream) architecture, using a set of process that communicates via messages. PCEM is implemented using parallel schemes of traditional classifiers, for the mixture of experts, and using a parallel version of a Genetic Algorithm to implement a voting weighted criterion. The PCEM is a novel algorithm since it allows us to classify large amounts of data with low execution times and high performance measures, which makes it an excellent tool for in classification of large amounts of data. A series of tests were performed with well known databases that allowed us to measure how PCEM performs with many datasets and how well it does compared with other systems available. Copyright © 2014 SCITEPRESS.

关键词： data mining

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3rd international conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA-2014

3rd International Conference on Frontiers in Intelligent Com...

引用

3rd international conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA-2014

ISBN: (纸本)9783319119328

The proceedings contain 95 papers. The special focus in this conference is on data Warehousing and mining, machine learning, Mobile and Ubiquitous Computing, AI, E-commerce and Distributed Computing and Soft Computing, Evolutionary Computing, Bio-inspired Computing and *** topics include: Approximation neural network for phoneme synthesis;pattern-matching for speaker verification;online clustering algorithm for restructuring user web search results;optimal computer based analysis for detecting malarial parasites;a survey of dynamic program analysis techniques and tools;extended self organizing map with probabilistic neural network for pattern classification problems;fuzzy rule-based adaptive proportional derivative controller;sentence completion using text prediction systems;architectural recommendations in building a network based secure, scalable and interoperable internet of things middleware;mean interleaved round robin algorithm;cryptanalysis of image encryption based on permutation-substitution using chaotic map and Latin square image cipher;windowed Huffman coding with limited distinct symbols by least recently used symbol removable;theme interception sequence learning;energy efficient distributed topology control technique with edge pruning;dynamic cache resizing in flashcache;heuristic for context detection in time varying domains;construction of automated concept map of learning using hashing technique;analysis of estimation techniques for feature based navigation of unmanned air vehicles;curved videotext detection and extraction;an approach to utilize FMEA for autonomous vehicles to forecast decision outcome;diffusion and encryption of digital image using genetic algorithm;character recognition using teaching learning based optimization;the fuzzy robust graph coloring problem;detection of moving object and image steganography using integer wavelet transform based on color space approach.

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A Multi-path Strategy for Hierarchical Ensemble Classification

A Multi-path Strategy for Hierarchical Ensemble Classificati...

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10th international conference on machine learning and data mining (MLDM)

作者： Alshdaifat, Esra'a Coenen, Frans Dures, Keith Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England

ISBN: (纸本)9783319089799;9783319089782

A solution to the multi-class classification problem is proposed founded on the concept of an ensemble of classifiers arranged in a hierarchical binary tree formation. An issue with this solution is that if a miss-classification occurs early on in the process (near the start of the hierarchy) there is no possibility of rectifying this error later on in the process. To address this issue a multi-path strategy is investigated based on the idea of using Classification Association Rule Miners at individual nodes. The conjectured advantage offered is that the confidence values associated with this form of classifier can be used to inform the proposed multi-path strategy. More specifically the confidence values are used to determine, at each node, whether one or two paths should be followed.

关键词： Hierarchical classification ensemble classification multi-class classification Classification Association Rule mining

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Applications of Concurrent Sequential patterns in Protein data mining

Applications of Concurrent Sequential Patterns in Protein Da...

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10th international conference on machine learning and data mining (MLDM)

作者： Wang, Cuiqing Lu, Jing Keech, Malcolm Shenyang Univ Chem Technol Shenyang 110142 Peoples R China Southampton Solent Univ Southampton SO14 0YN Hants England Univ Bedfordshire Luton LU1 3JU Beds England

ISBN: (纸本)9783319089799;9783319089782

Protein sequences of the same family typically share common patterns which imply their structural function and biological relationship. Traditional sequential patterns mining has its focus on mining frequently occurring sub-sequences. However, a number of applications motivate the search for more structured patterns, such as protein motif mining. This paper builds on the original idea of structural relation patterns and applies the Concurrent Sequential patterns (ConSP) mining approach in bioinformatics. Specifically, a new method and algorithms are presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Experiments with real-world protein datasets highlight the applicability of the ConSP methodology in protein data mining. The results show the potential for knowledge discovery in the field of protein structure identification.

关键词： protein sequences data mining concurrent sequential patterns (ConSP) bioinformatics ConSP mining concurrent vector method PROSITE knowledge discovery

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Comments data mining for Evaluating Student's Performance 3

Comments Data Mining for Evaluating Student's Performance

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3rd IIAI international conference on Advanced Applied Informatics (IIAI-AAI)

作者： Sorour, Shaymaa E. Mine, Tsunenori Goda, Kazumasa Hirokawa, Sachio Kafr Elsheik Univ Fac Specif Educ Kafrelsheikh Egypt Grad Sch Informat Sci & Elect Engn Nishi Ku Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Nishi Ku Fukuoka 812 Japan Kyushu Inst Informat Sci Fukuoka Japan Kyushu Univ Res Inst Informat Technol Higashi Ku Fukuoka 812 Japan

ISBN: (纸本)9781479941735

The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences between the comments in the two classes. The proposed methods basically employ latent semantic analysis (LSA) and two types of machine learning technique: SVM (support vector machine) and ANN (artificial neural network) for predicting students' final results in four grades of S, A, B and C. Moreover, an overlap method was proposed to improve the accuracy prediction results, the method allows to accept two grades for one mark to get the correct relation between LSA results and students' grades. The proposed methods achieve 50.7% and 48.7% prediction accuracy of students' grades by SVM and ANN, respectively. To this end, the results of this study reported models of students' academic performance predictors that are valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities.

关键词： Free-style comments Latent semantic analysis (LSA) Artificial neural network (ANN) Support vector machine (SVM) Overlap method

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Unsupervised relevance analysis for feature extraction and selection: A distance-based approach for feature relevance

Unsupervised relevance analysis for feature extraction and s...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Peluffo, Diego H. Lee, John A. Verleysen, Michel Rodríguez, José L. Castellanos-Domínguez, Germán Machine Learning Group ICTEAM Université catholique de Louvain Place du Levant 3 B-1348 Louvain-la-Neuve Belgium Molecular Imaging Radiotherapy and Oncology IREC Université catholique de Louvain Avenue Hippocrate 55 B-1200 Bruxelles Belgium Grupo de Automática Antigua estacíon del Ferrocarril Universidad Autónoma de Manizales Manizales Colombia Signal Processing and Recognition Group Universidad Nacional de Colombia 7 Via al Magdalena Manizales Colombia

ISBN: (纸本)9789897580185

The aim of this paper is to propose a new generalized formulation for feature extraction based on distances from a feature relevance point of view. This is done within an unsupervised framework. To do so, it is first outlined the formal concept of feature relevance. Then, a novel feature extraction approach is introduced. Such an approach employs the M-norm as a distance measure. It is demonstrated that under some conditions, this method can readily explain literature methods. As another contribution of this paper, we propose an elegant feature ranking approach for feature selection followed from the spectral analysis of the data variability. Also, we provide a weighted PCA scheme revealing the relationship between feature extraction and feature selection. To assess the behavior of the studied methods within a pattern recognition system, a clustering stage is carried out. Normalized mutual information is used to quantify the quality of resultant clusters. Proposed methods reach comparable results with respect to literature methods. Copyright © 2014 SCITEPRESS.

关键词： Extraction

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3D shape retrieval using uncertain semantic query a preliminary study

3D shape retrieval using uncertain semantic query a prelimin...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Aboubacar, Hattoibe Barra, Vincent Loosli, Gaëlle LIMOS Clermont Université BP 10448 F-63000 Clermont-Ferrand France CNRS UMR 6158 Limos F-63171 Aubiere France

ISBN: (纸本)9789897580185

The recent technological progress contributes to a huge increase of 3D models available in digital forms. Numerous applications were developed to deal with this amount of information, especially for 3D shape retrieval. One of the main issues is to break the semantic gap between shapes desired by users and shapes returned by retrieval methods. In this paper, we propose an algorithm to address this issue. First the user gives a semantic request. Second, a fuzzy 3D-shape generator sketches out suitable 3D-shapes. Those shapes are filtered by the user or a learning machine to select the ones that match the semantic query. Then, we use a state-of-the-art retrieval method to return real-world 3D shapes that match this semantic query. This algorithm is used to retrieve object in SHREC'07 database. The results are good and promising. Copyright © 2014 SCITEPRESS.

关键词： Semantics

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A Robust One-Class Classification Model with Interval-Valued data Based on Belief Functions and Minimax Strategy

A Robust One-Class Classification Model with Interval-Valued...

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10th international conference on machine learning and data mining (MLDM)

作者： Utkin, Lev V. Zhuk, Yulia A. Chekh, Anatoly I. St Petersburg State Forest Tech Univ Dept Control Automat & Syst Anal St Petersburg Russia Saint Petersburg State Elect Univ Dept Comp Sci St Petersburg Russia

ISBN: (纸本)9783319089799;9783319089782

A robust model for solving the one-class classification problem by interval-valued training data is proposed in the paper. It is based on using Dempster-Shafer theory for getting the lower and upper risk measures. The minimax or pessimistic strategy is applied to state an optimization problem in the framework of the modified support vector machine (SVM). The algorithm for computing optimal parameters of the classification function stems from extreme points of the convex polytope produced by the interval-valued elements of a training set. It is shown that the hard non-linear optimization problem is reduced to a finite number of standard SVMs.

关键词： novelty detection support vector machine kernel interval-valued data minimax strategy quadratic programming Dempster-Shafer theory

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Kernel completion for learning consensus support vector machines in bandwidth-limited sensor networks

Kernel completion for learning consensus support vector mach...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Lee, Sangkyun Pölitz, Christian Fakultät für Informatik LS VIII Technische Universität Dortmund 44221 Dortmund Germany

ISBN: (纸本)9789897580185

Recent developments in sensor technology allows for capturing dynamic patterns in vehicle movements, temperature changes, and sea-level fluctuations, just to name a few. A usual way for decision making on sensor networks, such as detecting exceptional surface level changes across the Pacific ocean, involves collecting measurement data from all sensors to build a predictor in a central processing station. However, data collection becomes challenging when communication bandwidth is limited, due to communication distance or low-energy requirements. Also, such settings will introduce unfavorable latency for making predictions on unseen events. In this paper, we propose an alternative strategy for such scenarios, aiming to build a consensus support vector machine (SVM) in each sensor station by exchanging a small amount of sampled information from local kernel matrices amongst peers. Our method is based on decomposing a "global" kernel defined with all features into "local" kernels defined only with attributes stored in each sensor station, sampling few entries of the decomposed kernel matrices that belong to other stations, and filling in unsampled entries in kernel matrices by matrix completion. Experiments on benchmark data sets illustrate that a consensus SVM can be built in each station using limited communication, which is competent in prediction performance to an SVM built with accessing all features. Copyright © 2014 SCITEPRESS.

关键词： Support vector machines

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An ontology and pattern clustering approach for activity recognition in smart environments 3

An ontology and pattern clustering approach for activity rec...

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3rd international conference on Soft Computing for Problem Solving, SocProS 2013

作者： Gayathri, K.S. Elias, Susan Shivashankar, S. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur602117 India Ericsson Research India Chennai India

ISBN: (纸本)9788132217701

Activity recognition aims at modeling the occupants’ behavior by analyzing the sensor data collected from the smart environment. Though most of the activity recognition systems use supervised learning techniques for building such models there is a shift towards the unsupervised learning paradigm as the process of annotating and labeling the data is prone to errors. This paper proposes an Event pattern Activity Modeling Framework (EPAM) to identify the occupant activity pattern from the sensor data by using an unsupervised machine learning approach and further analysis is done with a knowledge driven approach. In the context of smart environments, an activity is considered as a sequence of events that are generated continuously from the sensor data. The segmentation algorithm proposed in EPAM is used to identify appropriate event patterns for an activity that are then grouped together using a pattern clustering algorithm that presents a hierarchy of activities. The set of activities of the occupant, observed in a smart environment is not always sequential but is highly interleaved and discontinuous. The proposed algorithm accommodates this valid factor by an innovative use of the Jaro Winkler similarity measure. The hierarchy of activity generated by the pattern clustering approach is used for activity modeling. Ontology based activity modeling is preferred over other modeling techniques because of its unified modeling, representation and semantically clear reasoning. The experimentalresults show that the proposed EPAM framework of segmentation, pattern clustering and ontological modeling is efficient and more effective than the existing approach of activity modeling. © Springer India 2014.

关键词： Supervised learning

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