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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 99 条记录，以下是31-40 订阅

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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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Applying data mining to Healthcare: A Study of Social Network of Physicians and Patient Journeys 12th

Applying Data Mining to Healthcare: A Study of Social Networ...

引用

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

作者： Kaushik, Shruti Choudhury, Abhinav Mallik, Kaustubh Moid, Anzer Dutt, Varun Indian Inst Technol Mandi Appl Cognit Sci Lab Mandi 175005 Himachal Prades India

ISBN: (纸本)9783319419206;9783319419190

In 2004, the US President launched an initiative to make healthcare medical records available electronically [27]. this initiative gives researchers an opportunity to study and mine healthcare data across hospitals, pharmacies, and physicians in order to improve the quality of care. Physicians can make better informed decisions regarding care of patients if physicians have proper understanding of patient journeys. In addition, physician healthcare decisions are influenced by their social networks. In this paper, we find patterns among patient journeys for pain medications from sickness to recovery or death. Next, we combine social network analysis and diffusion of innovation theory to analyze the diffusion patterns among physicians prescribing pain medications. Finally, we suggest an interactive visualization interface for visualizing demographic distribution of patients. the main implication of this research is a better understanding of patient journeys via data-mining and visualizations;and, improved decision-making by physicians in treating patients.

关键词： Diffusion of innovation Patient journey Social network analysis Physicians Visualization Pain medications

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Multiple Consensuses Clustering by Iterative Merging/Splitting of Clustering patterns 12th

Multiple Consensuses Clustering by Iterative Merging/Splitti...

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

作者： Al-najdi, Atheer Pasquier, Nicolas Precioso, Frederic Univ Nice Sophia Antipolis CNRS I3S UMR 7271 F-06900 Sophia Antipolis France

ISBN: (纸本)9783319419206;9783319419190

the existence of many clustering algorithms with variable performance on each dataset made the clustering task difficult. Consensus clustering tries to solve this problem by combining the partitions generated by different algorithms to build a new solution that is more stable and achieves better results. In this work, we propose a new consensus method that, unlike others, give more insight on the relations between the different partitions in the clusterings ensemble, by using the frequent closed itemsets technique, usually used for association rules discovery. Instead of generating one consensus, our method generates multiple consensuses based on varying the number of base clusterings, and links these solutions in a hierarchical representation that eases the selection of the best clustering. this hierarchical view also provides an analysis tool, for example to discover strong clusters or outlier instances.

关键词： Unsupervised learning Clustering Consensus clustering Ensemble clustering Frequent closed itemsets

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A learning Framework to Improve Unsupervised Gene Network Inference 1

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

作者： Turki, Turki Bassett, William Wang, Jason T. L. King Abdulaziz Univ Dept Comp Sci POB 80221 Jeddah 21589 Saudi Arabia New Jersey Inst Technol Bioinformat Program Newark NJ 07102 USA Dept Comp Sci Newark NJ 07102 USA

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsupervised methods have been widely used to infer GRNs;however, these methods often create missing and spurious links. In this paper, we propose a learning framework to improve the unsupervised methods. Given a network constructed by an unsupervised method, the proposed framework employs a graph sparsification technique for network sampling and principal component analysis for feature selection to obtain better quality training data, which guides three classifiers to predict and clean the links of the given network. the three classifiers include neural networks, random forests and support vector machines. Experimental results on several datasets demonstrate the good performance of the proposed learning framework and the classifiers used in the framework.

关键词： Feature selection Graph mining Network analysis Applications in biology and medicine

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Statistical learning on Manifold-Valued data 12th

Statistical Learning on Manifold-Valued Data

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

作者： Kuleshov, Alexander Bernstein, Alexander Skolkovo Inst Sci & Technol Moscow Russia FRC CSC RAS Inst Syst Anal Moscow Russia Kharkevich Inst Informat Transmission Problems RA Moscow Russia

ISBN: (纸本)9783319419206;9783319419190

Regression on manifolds problem is to estimate an unknown smooth function f that maps p-dimensional manifold-valued inputs, whose values lie on unknown Input manifold M of lower dimensionality q < p embedded in an ambient high-dimensional input space R-p, to m-dimensional outputs from training sample consisting of given 'input-output' pairs. We consider this problem in which Jacobian J(f)(X) of function f and Input manifold M should be also estimated. the paper presents a new geometrically motivated method for estimating a triple (f(X), J(f)(X), M) from given sample. the proposed solution is based on solving a Tangent bundle manifold learning problem for specific unknown Regression manifold embedded in input-output space Rp+m and consisting of input-output pairs (X, f(X)), X is an element of M.

关键词： Regression on manifolds Regression on features Input manifold reconstruction Jacobian estimation Tangent bundle manifold learning

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On learning and Exploiting Time Domain Traffic patterns in Cellular Radio Access Networks 12th

On Learning and Exploiting Time Domain Traffic Patterns in C...

引用

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

作者： Perez-Romero, Jordi Sanchez-Gonzalez, Juan Sallent, Oriol Agusti, Ramon Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9783319419206;9783319419190

this paper presents a vision of how the different management procedures of future Fifth Generation (5G) wireless networks can be built upon the pillar of artificial intelligence concepts. After a general description of a cellular network and its management functionalities, highlighting the trends towards automatization, the paper focuses on the particular case of extracting knowledge about the time domain traffic pattern of the cells deployed by an operator. A general methodology for supervised classification of this traffic pattern is presented and it is particularized in two applicability use cases. the first use case addresses the reduction of energy consumption in the cellular network by automatically identifying cells that are candidates to be switched-off when they serve low traffic. the second use case focuses on the spectrum planning and identifies the cells whose capacity can be boosted through additional unlicensed spectrum. In both cases the outcomes of different classification tools are assessed. this capability to automatically classify cells according to some expert guidance is fundamental in future networks, where an operator deploys tenths of thousands of cells, so manual intervention of the expert is unfeasible.

关键词： Classification Cellular networks 5G Radio access network management

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EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets 1

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

作者： Fournier-Viger, Philippe Zida, Souleymane Lin, Jerry Chun-Wei Wu, Cheng-Wei Tseng, Vincent S. Harbin Inst Technol Shenzhen Grad Sch Sch Nat Sci & Humanities Shenzhen Peoples R China Univ Moncton Dept Comp Sci Moncton NB Canada Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Shenzhen Peoples R China Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To provide a concise and lossless representation of results to the user, the concept of closed high-utility itemsets was proposed. However, mining closed high-utility itemsets is computationally expensive. To address this issue, we present a novel algorithm for discovering closed high-utility itemsets, named EFIM-Closed. this algorithm includes novel pruning strategies named closure jumping, forward closure checking and backward closure checking to prune non-closed high-utility itemsets. Furthermore, it also introduces novel utility upper-bounds and a transaction merging mechanism. Experimental results shows that EFIM-Closed can be more than an order of magnitude faster and consumes more than an order of magnitude less memory than the previous state-of-art CHUD algorithm.

关键词： pattern mining High-utility itemset Closed itemset

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A Review on Artificial Intelligence Based Parameter Forecasting for Soil-Water Content 12th

A Review on Artificial Intelligence Based Parameter Forecast...

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

作者： Ozcep, Ferhat Yildirim, Eray Tezel, Okan Asci, Metin Karabulut, Savas Ozcep, Tazegul Istanbul Univ Dept Geophys Engn Istanbul Turkey Sakarya Univ Dept Geophys Engn Sakarya Turkey Kocaeli Univ Dept Geophys Engn Kocaeli Turkey Minist Natl Educ Sirinevler Mehmet Sen Okulu Istanbul Turkey

ISBN: (纸本)9783319419206;9783319419190

the purpose of this study, by using an artificial intelligent approaches, is to compare a correlation between geophysical and geotechnical parameters. the input variables for this system are the electrical resistivity reading, the water content laboratory measurements. the output variable is water content of soils. In this study, our data sets are clustered into 120 training sets and 28 testing sets for constructing the fuzzy system and validating the ability of system prediction, respectively. Relationships between soil water content and electrical parameters were obtained by curvilinear models. the ranges of our samples are changed between 1 - 50 ohm. m (for resistivity) and 20 - 60 (%, for water content). An artificial intelligent system (artificial neural networks, Fuzzy logic applications, Mamdani and Sugeno approaches) are based on some comparisons about correlation between electrical resistivity and soil-water content, for Istanbul and Golcuk Soils in Turkey.

关键词： Fuzzy logic applications AI Mamdani and Sugeno approaches Geophysical and geotechnical data

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IncMSTS-PP: An Algorithm to the Incremental Extraction of Significant Sequences from Environmental Sensor data 12th

IncMSTS-PP: An Algorithm to the Incremental Extraction of Si...

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

作者： Silveira Junior, Carlos Roberto Ribeiro, Marcela Xavier Prado Santos, Marilde Terezinha Univ Fed Sao Carlos Washington Luis Km 235 Sao Carlos SP Brazil

ISBN: (纸本)9783319419206;9783319419190

the mining of sequential patterns from environmental sensor data is a challenging task. the data can present noises and contain sparse patterns hide in a huge amount of information. the knowledge extracted from environmental sensor data can have many applications: indicate climate changes, risk of ecologic catastrophes and help to determine environment degradation in face of humans actions. However, there is a lack of methods that can handle this kind of data. Based on that, we proposed IncMSTS-PP: an incremental algorithm that finds sequential sparse patterns and enhances them semantically facilitating the interpretation. IncMSTS-PP implements STW-method to extract stretchy patterns (patterns with time gaps) in data with noises. the enhancement use post-processing method that generalizes the patterns using the fuzzy ontology knowledge. Our experiment shows that IncMSTS-PP extracts 2.3 times more relevant sequences than traditional algorithms in sensor domain. the post-processing summarizes the patterns reducing to 22.47% of the original number of patterns. In conclusion, IncMSTS-PP is efficient and reliable in the extraction of significant sequences from sensor data.

关键词： data mining Fuzzy ontology Real data Sequence generalization

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A Probabilistic Matrix Factorization Method for Link Sign Prediction in Social Networks 12th

A Probabilistic Matrix Factorization Method for Link Sign Pr...

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

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

ISBN: (纸本)9783319419206;9783319419190

In this paper, we consider the link sign prediction in social networks with friend and foe relationships. We view the sign prediction as a user-to-user recommendation problem with trust or distrust information. Not only do we take the topological relationships such as the social structural balance and status theories into consideration, but also the social factors that whether a user is trustworthy and whether the user easily trust others are involved. We propose a probabilistic matrix factorization method with social trust and distrust ensembles and the structural theories from social psychology in order to predict link signs in social networks. the experimental results show that our proposed method outperforms those of the previous studies on this problem.

关键词： Link sign prediction Matrix factorization Social psychology Signed networks

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