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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009"

共 98 条记录，以下是21-30 订阅

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Semantic Aware Bayesian Network Model for Actionable Knowledge Discovery in Linked data 1

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12th international conference on machine learning and data mining (mldm)

作者： Alharbi, Hasanein Saraee, Mohamad Univ Salford Salford M5 4WT Lancs England

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

the majority of the conventional mining algorithms treat the mining process as an isolated data-driven procedure and overlook the semantic of the targeted data. As a result, the generated patterns are abundant and end users cannot act upon them seamlessly. Furthermore, interdisciplinary knowledge can not be obtained from domain-specific silo of data. the emergence of Linked data (LD) as a new model for knowledge representation, which intertwines data with its semantics, has introduced new opportunities for data miners. Accordingly, this paper proposes an ontology-based Semantic-Aware Bayesian network (BN) model. In contraxt to the exisiting mining algorithms, the proposed model does nto transorm the original format of the LD set. therefore, it not only accomodates the sematnic aspects in LD, but also caters to the need of connectign different data-sets from different domains. We evaluate the proposed model on a Bone Dysplasia dataset, Experimental results show promising perfomance.

关键词： Linked data (LD) Actionable Knowledge Discovery (AKD) Bayesian Network (BN)

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Metadata-Based Clustered Multi-task learning for thread mining in Web Communities 12th

Metadata-Based Clustered Multi-task Learning for Thread Mini...

引用

12th international conference on machine learning and data mining (mldm)

作者： You, Qiang Wu, Ou Luo, Guan Hu, Weiming Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Natl Lab Pattern Recognit Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783319419206;9783319419190

With user-generated content explosively growing, how to find valuable posts from discussion threads in web communities becomes a hot topic. Although many learning algorithms have been proposed for mining the thread contents, there are still two problems that are not effectively considered. First, the learning algorithms are usually complicated so as to deal with various kinds of threads in web communities, which damages the generalization performance of the algorithms and takes the risk of overfitting to the learning models. Second, the small sample size problem exists when the training data for learning is divided into many isolated groups and each group is trained separately in order to avoid overfitting. In this paper, we propose a metadata-based clustered multi-task learning method, which takes full use of the metadata of threads and fuses it in the multi-task learning based on a divide-and-learn strategy. Our method provides an effective solution to the above problems by finding the geometric structure or context of semantics of threads in web communities and constructing the relations among training thread groups and their corresponding learning tasks. In addition, a soft-assigned clustered multi-task learning model is employed. Our experimental results show the effectiveness of our method.

关键词： Metadata thread mining Divide-and-learn Clustered multi-task learning Web community

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Rank Aggregation Algorithm Selection Meets Feature Selection 12th

Rank Aggregation Algorithm Selection Meets Feature Selection

引用

12th international conference on machine learning and data mining (mldm)

作者： Zabashta, Alexey Smetannikov, Ivan Filchenkov, Andrey ITMO Univ Dept Comp Sci 49 Kronverksky Pr St Petersburg 197101 Russia

ISBN: (纸本)9783319419206;9783319419190

Rank aggregation is the important task in many areas, and different rank aggregation algorithms are created to find optimal rank. Nevertheless, none of these algorithms is the best for all cases. the main goal of this work is to develop a method, which for each rank list defines, which rank aggregation algorithm is the best for this rank list. Canberra distance is used as a metric for determining the optimal ranking. three approaches are proposed in this paper and one of them has shown promising result. Also we discuss, how this approach can be applied to learn filtering feature selection algorithm ensemble.

关键词： Meta-learning Rank aggregation Ensemble learning Feature selection

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A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association 12th

A New Strategy for Case-Based Reasoning Retrieval Using Clas...

引用

12th international conference on machine learning and data mining (mldm)

作者： Aljuboori, Ahmed Meziane, Farid Parsons, David Univ Salford Sch Comp Sci & Engn Salford M5 4WT Lancs England

ISBN: (纸本)9783319419206;9783319419190

this paper proposes a novel strategy, Case-Based Reasoning Using Association Rules (CBRAR) to improve the performance of the Similarity base Retrieval SBR, classed frequent pattern trees FP-CAR algorithm, in order to disambiguate wrongly retrieved cases in Case-Based Reasoning (CBR). CBRAR use class association rules (CARs) to generate an optimum FP-tree which holds a value of each node. the possible advantage offered is that more efficient results can be gained when SBR returns uncertain answers. We compare the CBR Query as a pattern with FP-CAR patterns to identify the longest length of the voted class. If the patterns are matched, the proposed strategy can select not just the most similar case but the correct one. Our experimental evaluation on real data from the UCI repository indicates that the proposed CBRAR is a better approach when compared to the accuracy of the CBR systems used in our experiments.

关键词： Class Association Rules Frequent pattern trees Case-Based Reasoning Retrieval P-trees

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Feature Selection for Handling Concept Drift in the data Stream Classification 12th

Feature Selection for Handling Concept Drift in the Data Str...

引用

12th international conference on machine learning and data mining (mldm)

作者： Turkov, Pavel Krasotkina, Olga Mottl, Vadim Sychugov, Alexey Tula State Univ 92 Lenin Ave Tula 300600 Russia Moscow MV Lomonosov State Univ Moscow 119991 Russia Russian Acad Sci Ctr Comp 40 Vavilov St Moscow 119333 Russia

ISBN: (纸本)9783319419206;9783319419190

With the advance in both hardware and software technologies, streaming data is ubiquitous today, and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. One of difficult problems in the data stream domain is the data streams classification problem. the traditional classification algorithms have to be adapted to run in a streaming environment because of the underlying resource constraints in terms of memory and running time. there are at least three hard aspects in the data streams classification: large length, concept drift and feature selection. Concept drift is a common attribute of data streams that occurs as a result of changes in the underlying concepts. Feature selection has been extensively studied from a conventional mining perspective, but it is a much more challenging problem in the data stream domain. the concept drift and large length make impossible applying classical feature selection methods in the learning procedure. this paper proposes a new Bayesian framework to feature selection in data streams pattern recognition problem. We suggest a hierarchical probabilistic model with sparse regularization for estimation of decision rule parameters. the proposed approach gives a strong Bayesian formulation of the shrinkage criterion for predictor selection. Experimental results show that the proposed framework outperforms other methods of concept drift analysis.

关键词： Feature selection Concept drift Changing environment data streams classification Supervised learning pattern recognition

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Driving Style Identification with Unsupervised learning 1

引用

12th international conference on machine learning and data mining (mldm)

作者： Nikulin, Vladimir Vyatka State Univ Dept Math Methods Econ Kirov Russia

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

One way to optimise insurance prices and policies is to collect and to analyse driving trajectories: sequences of 2D-points, where time distance between any two consequitive points is a constant. Suppose that most of the drivers have safe driving style with similar statistical characteristics. Using above assumption as a main ground, we shall go through the list of all drivers (available in the database) assuming that the current driver is "bad". We shall add to the training database several randomly selected drivers assuming that they are "good". By comparing the current driver with a few randomly selected "good" drivers, we estimate the probability that the current driver is bad (or has significant deviations from usual statistical characteristics). Note as a distinguished particular feature of the presented method: it does not require availability of the training labels. the database includes 2736 drivers with 200 variable length driving trajectories each. We tested our model (with competitive results) online during Kaggle-based AXA Drivers Telematics Challenge in 2015.

关键词： Motion mining Unsupervised learning Similarity measures learning of similarity Big data Sequential data

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Heuristic Model to Improve Feature Selection Based on machine learning in data mining 6

Heuristic Model to Improve Feature Selection Based on Machin...

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6th international conference on Cloud System and Big data Engineering (Confluence)

作者： Majumdar, Jahin Mal, Anwesha Gupta, Shruti Am Univ ASET Dept CSE Noida India

ISBN: (纸本)9781467382038

data mining and machine learning is one of the most popular research areas in computer science that is relevant in today's world of unfathomable data. To keep up with the rising size of data, there arises a need to quickly extract knowledge from data sources to aid data analysis research and improve industry and market needs. Primary data mining algorithms like k-means, Apriori, PageRank etc. are used today, but machine learning techniques can enhance the same by learning from the complex patterns. this paper focuses on the various existing approaches where machine learning algorithms have been used to improve data classification and pattern recognition in data mining especially for Feature Selection. It compares and contrasts the existing techniques and finds out the best one among them. Further, the paper proposes a heuristic approach to theoretically overcome most of the limitations in existing algorithms.

关键词： data mining machine learning Fuzzy Knowledge Discovery

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Gossip-Based Behavioral Group Identification in Decentralized OSNs 12th

Gossip-Based Behavioral Group Identification in Decentralize...

引用

12th international conference on machine learning and data mining (mldm)

作者： Laleh, Naeimeh Carminati, Barbara Ferrari, Elena Girdzijauskas, Sarunas Univ Insubria DiSTA Varese Italy Royal Inst Technol KTH Stockholm Sweden

ISBN: (纸本)9783319419206;9783319419190

DOSNs are distributed systems providing social networking services that become extremely popular in recent years. In DOSNs, the aim is to give the users control over their data and keeping data locally to enhance privacy. therefore, identifying behavioral groups of users that share the same behavioral patterns in decentralized OSNs is challenging. In the fully distributed social graph, each user has only one feature vector and these vectors can not move to any central storage or other users in a raw form duo to privacy issues. We use a gossip learning approach where all users are involved with their local estimation of the clustering model and improve their estimations and finally converge to a final clustering model available for all users. In order to evaluate our approach, we implement our algorithm and test it in a real Facebook graph.

关键词： Decentralized Online Social Network (DOSN) Gossip learning Newscast EM Behavioral group identification

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Feature Reduction for Multi Label Classification of Discrete data 12th

Feature Reduction for Multi Label Classification of Discrete...

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12th international conference on machine learning and data mining (mldm)

作者： Devi, V. Susheela Akhand, Bhupesh Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

ISBN: (纸本)9783319419206;9783319419190

We describe a novel multi-label classification algorithm which works for discrete data. A matrix which gives the membership value of each discrete value of each attribute for every class. For a test pattern, looking at the values taken by each attribute, we find the subset of classes to which the pattern belongs. If the number of classes are large or the number of features are large, the space and time complexity of this algorithm will go up. To mitigate this problems, we have carried out feature selection before carrying out classification. We have compared two feature reduction techniques for getting good results. the results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results. Using feature reduction our classification accuracy and running time for algorithm is improved. the performance of the above algorithm is evaluated using some benchmark datasets and the results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results.

关键词： Classification (of information)

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Pruning a Random Forest by learning a learning Algorithm 12th

Pruning a Random Forest by Learning a Learning Algorithm

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12th international conference on machine learning and data mining (mldm)

作者： Dheenadayalan, Kumar Srinivasaraghavan, G. Muralidhara, V. N. Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9783319419206;9783319419190

Ensemble learning is a popular learning paradigm and finds its application in many diverse fields. Random Forest, a decision tree based ensemble learning algorithm has received constant attention in the research community due to its ability to learn complex rules and generalize well for unknown data. Identifying the number of base classifiers (trees) required for a particular dataset is one of the key questions addressed in this paper. Statistical analyses of individual base classifiers are carried out to prune the ensemble model without compromising the classification accuracy of the model. learning the learned model, i.e., learning the statistics of the forest in its entirety along with the information available in the dataset can reveal the optimal thresholds that should be used to prune an ensemble model. Experimental results reveal that, on an average 78% of the trees were pruned on 26 different datasets obtained from the UCI repository. the impact of pruning was positive with 22 out of 26 datasets showing equal or better classification accuracy in comparison with the Classical Random Forest algorithm.

关键词： Ensemble learning Random forest Pruning Matthews correlation coefficient Meta-learning

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