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8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Isaksson, Charlie Dunham, Margaret H. Hahsler, Michael Department of Computer Science and Engineering Southern Methodist University Dallas TX United States

ISBN: (纸本)9783642315367

In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). This algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also employs a novel cluster updating strategy which is inspired by competitive learning techniques developed for Self Organizing Maps (SOMs). In addition, SOStream has built-in online functionality to support advanced stream clustering operations including merging and fading. This makes SOStream completely online with no separate offline components. Experiments performed on KDD Cup'99 and artificial datasets indicate that SOStream is an effective and superior algorithm in creating clusters of higher purity while having lower space and time requirements compared to previous stream clustering algorithms. © 2012 Springer-Verlag.

关键词： Self organizing maps

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Improving network security using machine learning techniques

Improving network security using machine learning techniques

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2012 3rd IEEE international conference on Computational Intelligence and Computing Research, ICCIC 2012

作者： Akbar, Shaik Chandulal, J.A. Nageswara Rao, K. Sudheer Kumar, G. Department of Computer Science and Engineering SVIET Nandamuru Andhra Pradesh India Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering P.V.P. S. i .Technology Vijayawada Andhra Pradesh India

ISBN: (纸本)9781467324816

Discovery of malicious correlations in computer networks has been an emergent problem motivating extensive research in computer science to develop improved intrusion detecting systems (IDS). In this manuscript, we present a machine learning approach known as Decision Tree (C4.5) Algorithm and Genetic Algorithm, to classify such risky/attack type of connections. The algorithm obtains into consideration dissimilar features in network connections and to create a classification rule set. Every rule in rule set recognizes a particular attack type. For this research, we implement a GA, C.45 and educated it on the KDD Cup 99 data set to create a rule set that can be functional to the IDS to recognize and categorize dissimilar varieties of assault links. During our study, we have developed a rule set contain of six rules to classify six dissimilar attack type of connections that fall into 4 modules namely DoS, U2R, root to local and probing attacks. The rule produces works with 93.70% correctness for detecting the denial of service type of attack connections, and with significant accuracy for detecting the root to local, user to root and probe connections. Results from our experiment have given hopeful results towards applying enhanced genetic algorithm for NIDS. © 2012 IEEE.

关键词： data mining

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Features' weight learning towards improved query classification

Features' weight learning towards improved query classificat...

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3rd international conference on Autonomous and Intelligent Systems, AIS 2012

作者： Abghari, Arash Abida, Kacem Karray, Fakhri Electrical and Computer Engineering Department University of Waterloo Waterloo ON Canada

ISBN: (纸本)9783642313677

This paper is an attempt to enhance query classification in call routing applications. We have introduced a new method to learn weights from training data by means of regression model. In this work, we have tested our method with tf-idf weighting scheme, but the approach can be applied to any weighting scheme. Empirical evaluations with several classifiers including Support Vector machines (SVM), Maximum Entropy, Naive Bayes, and K-Nearest Neighbor (KNN) show substantial improvement in both macro and micro F1 measure. © 2012 Springer-Verlag.

关键词： Support vector machines

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Determining pattern similarity in a medical recommender system 3rd

Determining pattern similarity in a medical recommender syst...

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3rd international conference on data and Knowledge Engineering, ICDKE 2012

作者： Komkhao, Maytiyanin Lu, Jie Zhang, Lichen Fernuniversität in Hagen Germany School of Software University of Technology Sydney P.O. Box 123 BroadwayNSW2007 Australia School of Information Engineering Guangdong University of Technology 100 Waihuan Xi Road Panyu District Guangzhou China

ISBN: (纸本)9783642346781

As recommender systems have proven their effectiveness in other areas, it is aimed to transfer this approach for use in medicine. Particularly, the diagnoses of physicians made in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge in order to reduce the risk to patients. Recommendation is mainly based on finding known patterns similar to a case under consideration. To search for such patterns in rather large databases, a weighted similarity distance is employed, which is specially derived for medical knowledge. For collaborative filtering an incremental algorithm, called W-InCF, is used working with the Mahalanobis distance and fuzzy membership. W-InCF consists of a learning phase, in which a cluster model of patients’ medical history is constructed incrementally, and a prediction phase, in which the medical pattern of each patient considered is compared with the model to determine the most similar cluster. Fuzzy sets are employed to cope with possible confusion of decision making on overlapping clusters. The degrees of membership to these fuzzy sets is expressed by a weighted Mahalanobis radial basis function, and the weights are derived from risk factors identified by experts. The algorithm is validated using data on cephalopelvic disproportion. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Fuzzy sets

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Outcome prediction for patients with severe traumatic brain injury using permutation entropy analysis of electronic vital signs data

Outcome prediction for patients with severe traumatic brain ...

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8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Kalpakis, Konstantinos Yang, Shiming Hu, Peter F. MacKenzie, Colin F. Stansbury, Lynn G. Stein, Deborah M. Scalea, Thomas M. Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle Baltimore MD 21250 United States R. Adams Cowley Shock Trauma Center Shock Trauma and Anesthesiology Research Center University of Maryland School of Medicine Baltimore MD 21201 United States

ISBN: (纸本)9783642315367

Permutation entropy is computationally efficient, robust to noise, and effective to measure complexity. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (TBI). Using permutation entropy calculated from early vital signs (initial 10~20% of patient hospital stay time), we built classifiers to predict in-hospital mortality, and mobility measured by 3-month Extended Glasgow Outcome Score (GOSE). Sixty patients with severe TBI produced a skewed dataset that we evaluated for accuracy, sensitivity and specificity. With early vital signs data, the overall prediction accuracy achieved 91.67% for mortality, and 76.67% for 3-month GOSE in testing datasets, using the leave-one-out cross validation. We also applied Receiver Operating Characteristic analysis to compare classifiers built from different learning methods. Those results support the applicability of permutation entropy in analyzing the dynamic behavior of biomedical time series for early prediction of mortality and long-term patient outcomes. © 2012 Springer-Verlag.

关键词： Hospitals

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Autonomous and Intelligent Systems - Third international conference, AIS 2012, Proceedings

Autonomous and Intelligent Systems - Third International Con...

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3rd international conference on Autonomous and Intelligent Systems, AIS 2012

ISBN: (纸本)9783642313677

The proceedings contain 31 papers. The topics discussed include: spatially correlated multi-modal wireless sensor networks: a coalitional game theoretic approach;autonomous self-aligning and self-calibrating capacitive sensor system;relay node positioning in wireless sensor networks by means of evolutionary techniques;vehicular ad-hoc networks(VANETs): capabilities, challenges in information gathering and data fusion;multi-cue based place learning for mobile robot navigation;bio-inspired navigation of mobile robots;market-based framework for mobile surveillance systems;tactical resource planner for workforce allocation in telecommunications;an interval type-2 fuzzy logic system for the modeling and prediction of financial applications;adaptive fuzzy logic control for time-delayed bilateral teleoperation;and an interval type-2 fuzzy logic system for human silhouette extraction in dynamic environments.

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A Robust Energy Artificial Neuron Based Incremental Self-Organizing Neural Network with a Dynamic Structure

A Robust Energy Artificial Neuron Based Incremental Self-Org...

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IEEE international conference on Systems, Man, and Cybernetics

作者： Hao Liu Haruhiko Sato Satoshi Oyama Masahito Kurihara Graduate School of Information Science and Technology Hokkaido University

ISBN: (纸本)9781467317139

Self-organizing neural network which is an unsupervised learning algorithm is to discover the inherent relationships of data. Such technique has become an important tool for data mining, machine learning and pattern recognition. Most self-organizing neural networks have a difficulty in reflecting data distributions precisely if data distributions are very complex. And meanwhile, it is also hard for these algorithms to learn new data incrementally without destroying the previous learnt data. In this paper, we propose a robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure (REISOD). It can adjust the scale of network automatically to adapt the scale of the data set and learn new data incrementally with preserving the former learnt results. Moreover, several experiments show that our algorithm can reflect data distributions precisely.

关键词： Self-organizing Unsupervised learning Neural network Incremental learning SOM REISOD data distribution Incremental Artificial neuron Neural network dataset new data pattern recognition

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Multi-task co-clustering via nonnegative matrix factorization

Multi-task co-clustering via nonnegative matrix factorizatio...

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international conference on pattern recognition

作者： Saining Xie Hongtao Lu Yangcheng He Shanghai Jiaotong University China

ISBN: (纸本)9781467322164

Recent results have empirically proved that, given several related tasks with different data distributions and an algorithm that can utilize both the task-specific and cross-task knowledge, clustering performance of each task can be significantly enhanced. This kind of unsupervised learning method is called multi-task clustering. We focus on tackling the multi-task clustering problem via a 3-factor nonnegative matrix factorization. The object of our approach consists of two parts: (1) Within-task co-clustering: co-cluster the data in the input space individually. (2) Cross-task regularization: Learn and refine the relations of feature spaces among different tasks. We show that our approach has a sound information theoretic background and the experimental evaluation shows that it outperforms many state-of-the-art single-task or multi-task clustering methods.

关键词： Clustering algorithms Joints data mining Mutual information Probability distribution Educational institutions machine learning

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Artificial Intelligence and Computational Intelligence 2012

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丛书名： Lecture Notes in Computer Science

2012年

作者： Jingsheng Lei Fu Lee Wang Hepu Deng Duoqian Miao

ISBN: (数字)9783642334788

ISBN: (纸本)9783642334771

This volume proceedings contains revised selected papers from the 4th international conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. The total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. The papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

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Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

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NEUROCOMPUTING 2011年第8期74卷 1237-1242页

作者： Maravall, Dario de Lope, Javier Univ Politecn Madrid Ctr Automat & Robot CSIC Dept Appl Intelligent Syst Madrid Spain Univ Politecn Madrid Dept Artificial Intelligence Fac Comp Sci Sch Telecommun Engn Madrid Spain

In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between presynaptic and post-synaptic learning in artificial neural networks. Tc illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. The main, novel contribution of ANLAGIS is the incorporation of learning Automata Theory within its structure;the paper includes also a novel learning scheme for stochastic learning automata. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Granular computing learning automata theory pattern recognition machine learning

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