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Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on

Date 5-8 Dec. 2004

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  • Fourth International Conference on Hybrid Intelligent Systems

    Publication Year: 2004
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2004 , Page(s): v - x
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  • General Chairs' Welcome Message

    Publication Year: 2004 , Page(s): xi - xii
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  • Program Chairs' Welcome Message

    Publication Year: 2004 , Page(s): xiii
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  • Conference Organization

    Publication Year: 2004 , Page(s): xiv - xv
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  • Emergent Design: Opportunities for Hybridizing Agent-Based and Evolutionary Computation

    Publication Year: 2004 , Page(s): 2
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  • Discovering Rules of Adaptation and Interaction: From Molecules and Gene Interaction to Brain Functions

    Publication Year: 2004 , Page(s): 3
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  • Granular Modeling: The Synergy of Granular Computing and Fuzzy Logic

    Publication Year: 2004 , Page(s): 4
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  • Hybrid Intelligent Techniques for Intelligent Personal Assistant in Digital Convergence

    Publication Year: 2004 , Page(s): 5
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  • Intelligent Robotic Systems: Safety, Security, Health, and Dependability

    Publication Year: 2004 , Page(s): 6
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  • Margin-based active learning and background knowledge in text mining

    Publication Year: 2004 , Page(s): 8 - 13
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (352 KB) |  | HTML iconHTML  

    Text mining, also known as intelligent text analysis, text data mining or knowledge-discovery in text, refers generally to the process of extracting interesting and nontrivial information and knowledge from text. One of the main problems with text mining and classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data (Kiritchenko and Matwin 2001). Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we evaluate the benefits of introducing unlabeled data in a support vector machine automatic text classifier. We further evaluate the possibility of learning actively and propose a method for choosing the samples to be learned. View full abstract»

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  • Block learning Bayesian network structure from data

    Publication Year: 2004 , Page(s): 14 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (128 KB) |  | HTML iconHTML  

    Existing methods for learning Bayesian network structures run into the computational and statistical problems because of the following two reasons: a large number of variables and a small sample size for enormous variables. Adopting the divide and conquer strategies, we propose a novel algorithm, called block learning algorithm, to learn Bayesian network structures. The method partitions the variables into several blocks that are overlapped with each other. The blocks are learned individually with some constraints obtained from the learned overlap structures. After that, the whole network is recovered by combining the learned blocks. Comparing with some typical learning algorithms on golden Bayesian networks, our proposed methods are efficient and effective. It shows a large potential capability to be scaled up. View full abstract»

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  • Robotic hand-eye coordination: from observation to manipulation

    Publication Year: 2004 , Page(s): 20 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    In this paper, we present a new hybrid method of performing eye-to-hand coordination and manipulation to produce a working robot named COERSU. The method is an optimized combination of two neuro-fuzzy approaches developed by the authors: direct fuzzy servoing and fuzzy correction. The fuzzy methods are tuned by an adaptive neuro-fuzzy inference system (ANFIS). On the whole, a genetic tuner and two neuro-fuzzy networks contribute to find the final optimum position of the robotic tooltip in order to grasp the target. Experimental results from COERSU in a table-top scenario to manipulate some soft objects (e.g. fruit/egg) also validate the method. View full abstract»

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  • An intelligent model for reconstruction of stance time from faulty gait recording

    Publication Year: 2004 , Page(s): 26 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (160 KB) |  | HTML iconHTML  

    In an erroneous footfall ground-reaction force-time recording, which may occur for people with disabilities or frail elderly individuals, the stance time (ST) can be either corrupted or missing. Previous methods to estimate missing ST require force-time data from multiple force platforms and are affected by inter-step variability. This paper presents a model based on support vector machine (SVM) that is capable of estimating the missing ST from the available vertical force-timing characteristics with significantly high accuracy. The model was built using features taken from a data set of 466 sample trials of 27 subjects. A test on 40 sample trials drawn from all the subjects revealed an average prediction accuracy of 96.63% (±2.89%). In one-fourth of the test trials, the prediction error was within 1.0%. The model achieves considerable improvement over an artificial neural network based model built and tested on the same data set. The effect of kernel junction parameters and ε-insensitive loss function on prediction error is also analysed and presented. View full abstract»

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  • Comparisons between heuristics based on correlativity and efficiency for landmarker generation

    Publication Year: 2004 , Page(s): 32 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (120 KB) |  | HTML iconHTML  

    Recently, we proposed a new meta-learning approach based on landmarking. This approach, which utilises a new set of criteria for selecting landmarkers, generates a set of landmarkers that are each functions over the performance over subsets of the candidate algorithms being landmarked. In this paper, we experiment with three heuristics based on correlativity and efficiency. With each heuristic, the landmarkers generated using linear regression are able to estimate accuracy well, even when only utilising a small fraction of the given algorithms. The results also show that the heuristic in which efficiencies are estimated via 1-nearest neighbour outperformed the other heuristics. View full abstract»

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  • Reconfigurable amplifier circuits for adaptive sensor systems employing bio-inspiration

    Publication Year: 2004 , Page(s): 38 - 43
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (192 KB) |  | HTML iconHTML  

    In particular primary electronics of sensor systems is strongly subject to deviations and degradations caused by environmental and manufacturing conditions. Current approaches cope with these challenges by calibration or trimming techniques. More recent approaches from the field of evolutionary electronics offer considerable extensions, incorporating also issues of fault-tolerance and self-repair. State-of-the-art evolvable analog hardware bases on field-programmable-transistor-arrays (FPTA) and start from primal soup for each new problem. Our work deals with the crucial issue to efficiently include the wealth of existing engineering design knowledge into the otherwise attractive concept. For the practically relevant task of sensor amplifiers, a particular flexible FPTA architecture is developed, verified and physically implemented in a 0.35 μm CMOS technology. View full abstract»

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  • An empirical performance comparison of machine learning methods for spam e-mail categorization

    Publication Year: 2004 , Page(s): 44 - 48
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning algorithms applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful information for all the machine learning algorithms considered to detect spam e-mail. View full abstract»

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  • Support vector machine and generalized regression neural network based classification fusion models for cancer diagnosis

    Publication Year: 2004 , Page(s): 49 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    This paper presents decision-based fusion models to classify BRCA1, BRCA2 and Sporadic genetic mutations for breast and ovarian cancer. Different ensembles of base classifiers using the stacked generalization technique have been proposed including support vector machines (SVM) with linear, polynomial and radial base function kernels. A generalized regression neural network (GRNN) is then applied to predict the mutation type based on the outputs of base classifiers, and experimental results show that the new proposed fusion methodology for selecting the best and removing weak classifiers outperforms single classification models. View full abstract»

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  • Discovering operational signatures with time constraints from a discrete event sequence

    Publication Year: 2004 , Page(s): 55 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (160 KB) |  | HTML iconHTML  

    This paper aims at showing a method to discover signatures (or models of chronicles) from a discrete event sequence (alarms) generated by a monitoring cognitive agent (MCA). When the counting process of the events generated by a couple (process, MCA) behaves like a Poisson process, this couple can be considered as stochastic discrete event generator SDEG(pr, MCA) and modeled as a superposition of Poisson and an homogeneous discrete time Markov chain. The 'BJT' algorithm uses these two representations in order to help in the discovering of signatures. The results obtained on an industrial process monitored with a Sachem system have been validated by experts, confirming so the relevance of the approach within an industrial frame. View full abstract»

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  • Learning diagnosis profiles through semi-supervised gradient descent of hidden Markov models

    Publication Year: 2004 , Page(s): 61 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (200 KB) |  | HTML iconHTML  

    In this paper, we consider the problem of adapting the model of a diagnosis-helping module, which interacts with human experts. The approach consists of enforcing strong semantics in the model, so that this interaction may be as intuitive as possible. When learning the model, the problem consists in respecting these semantics while learning with few data. We addressed this problem through a semisupervised gradient descent algorithm applied to partially observable Markov models with fuzzy observations. This method optimizes several criteria at once, guiding the search to a compromise between the expert's directives and objective evaluations. This method has been successfully applied to a tele-medicine application where the system monitors dialyzed patients and alerts nephrologists. View full abstract»

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  • Downward refinement in the ALN description logic

    Publication Year: 2004 , Page(s): 68 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (160 KB) |  | HTML iconHTML  

    We focus on the problem of specialization in a description logics (DL) representation, specifically the ALN language. Standard approaches to learning in these representations are based on bottom-up algorithms that employ the lcs operator, which, in turn, produces overly specific (overfitting,) and still redundant concept definitions. In the dual (top-down) perspective, this issue can be tackled by means of an ILP downward operator. Indeed, using a mapping from DL descriptions onto a clausal representation, we define a specialization operator computing maximal specializations of a concept description on the grounds of the available positive and negative examples. View full abstract»

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  • Hybrid soft categorization in conceptual spaces

    Publication Year: 2004 , Page(s): 74 - 79
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB) |  | HTML iconHTML  

    Understanding the process of categorization is of great importance for building intelligent agents. Formulated categories help agents find information easier and understand the external world better. Instance-based categorization and prototype-based categorization have been two dominant approaches in the AI community. However, they share some drawbacks in common. First, they are crisp boundary-based hard categorizations (similar to classification). Second, they are not well-suited for dynamic category learning and formation. We propose a hybrid soft categorization in the conceptual level that overcomes these drawbacks. The hybrid soft categorization merges the two popular hard categorizations and provides a robust fuzzy boundary-based soft categorization. View full abstract»

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  • Experience based reasoning for recognising fraud and deception

    Publication Year: 2004 , Page(s): 80 - 85
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    Fraud, deception and their recognition have received increasing attention in multiagent systems (MAS), e-commerce, and agent societies. However, little attention has been given to the theoretical foundation for fraud and deception from a logical viewpoint. We fill this gap by arguing that experience-based reasoning (EBR) is a logical foundation for recognizing fraud and deception. It provides a logical analysis of deception, which classifies recognition of deception into knowledge-based deception recognition, inference-based deception recognition, and hybrid deception recognition. It will examine the relationship between EBR and fraud as well as deception. It uses EBR to recognize fraud and deception in e-commerce and MAS. The proposed approach will facilitate research and development of recognition of fraud and deception in e-commerce. View full abstract»

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  • An efficient feature selection using multi-criteria in text categorization

    Publication Year: 2004 , Page(s): 86 - 91
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (128 KB) |  | HTML iconHTML  

    Text categorization is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on multicriteria ranking of features. Based on a threshold value for each criterion, a new procedure for feature selection is proposed and applied to a text categorization. Experiments dealing with the Reuters-21578 benchmark data and the naive Bayes algorithm show that the proposed approach outperforms performances in compare to conventional feature selection methods. View full abstract»

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  • Behavior modeling using a hierarchical HMM approach

    Publication Year: 2004 , Page(s): 92 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (144 KB) |  | HTML iconHTML  

    We introduce a new methodology for the hierarchical modeling of the behavior-with-time of players operating and interacting within a certain application domain. Behavior modelling and characterization are performed online, given that a number of observations are made or sensed at regular time intervals with respect to each player. A key element of this hierarchical behavior modeling system architecture is a new formulation of multiple hidden Markov models (HMM) with discrete densities operating in parallel, with each HMM accepting a single feature-related observation sequence. However the proposed classification approach recognizes the existence of possible dependencies between the observation sequences of the features obtained for a given player. This property is effectively exploited in a new dependent-multiHMM with discrete densities (DM-HMM-D) classification approach. The proposed methodology is applied in modeling the behavior of aircrafts operating in relatively simple 3D "air-patrol" situations. Computer simulation results demonstrate the significant gains that can be obtained in system classification and modeling performance when compared to those obtained while using conventional independent-multidiscrete hidden Markov model (IM-HMM-D) schemes. View full abstract»

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