this study proposes two kinds of Evolutionary Data Mining (EvoDM) algorithms to the insurance fraud *** is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA).the other is MPSO-Kmeans by combining K-m...
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this study proposes two kinds of Evolutionary Data Mining (EvoDM) algorithms to the insurance fraud *** is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA).the other is MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO).the dataset used in this study is composed of 6 attributes with 5000 instances for car insurance *** 5000 instances are divided into 4000 training data and 1000 test *** different initial cluster centers for each attribute are set by means of (a) selecting the centers randomly from the training set and (b) averaging all data of training set ***,the proposed GA-Kmeans and MPSO-Kmeans are employed to determine the optimal weights and final cluster centers for attributes,and the accuracy of prediction for test set is computed based on the optimal weights and final cluster *** show that the presented two EvoDM algorithms significantly enhance the accuracy of insurance fraud prediction when compared the results to that of pure K-means algorithm.
application-level protocol identification has attracted great interests in academia and become a relatively independent research realm. Withthe rapid development of Internet and the protocols complicated day by day, ...
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the proceedings contain 85 papers. the topics discussed include: application of low interaction honeypot for analysis of Internet malicious activity;evaluation of a fragment-optimized content aggregation web system;fa...
ISBN:
(纸本)9780946881680
the proceedings contain 85 papers. the topics discussed include: application of low interaction honeypot for analysis of Internet malicious activity;evaluation of a fragment-optimized content aggregation web system;faking the real and realizing the fake: from virtual reality to hyperreality;a machine-learning approach for workflow identification from low-level monitoring information;empirical analysis of some cryptographic algorithms;polaritytrust: measuring trust and reputation in social networks;user interface design within a mobile educational game;distributed ontology based infrastructure : focus in e-government;is your virtualized network really what you think it is?;teleoperation of mobile robots over wireless Internet;a method of resolving objects' location in an autonomous distributed computing environment;and context-dependent opinion retrieval for high precision results at top documents.
In this paper we propose a constructivist theory, model and application for the design and development of a Game-Based learningapplication introducing virtual 3D dinosaurs science. How to design effective learning op...
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Withthe rapid development of multi-media and computer technology, English teaching aided by multimedia computers has made great progress. this paper mainly introduces a new English writing approach and how to make us...
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Withthe popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has b...
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ISBN:
(纸本)9781450304931
Withthe popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machinelearning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric learning (UDML) scheme, which not only exploits both visual and textual contents of social images, but also effectively unifies both inductive and transductive metric learning techniques in a systematic learning framework. We further develop an efficient stochastic gradient descent algorithm for solving the UDML optimization task and prove the convergence of the algorithm. By applying the proposed technique to the automated image tagging task in our experiments, we demonstrate that our technique is empirically effective and promising for mining social images towards some real applications. Copyright 2011 ACM.
As novel forms of educational software continue to be created, it is often difficult to understand a priori which ensemble of interaction behaviours is conducive to learning. In this paper, we describe a user modeling...
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ISBN:
(纸本)9789038625379
As novel forms of educational software continue to be created, it is often difficult to understand a priori which ensemble of interaction behaviours is conducive to learning. In this paper, we describe a user modeling framework that relies on interaction logs to identify different types of learners, as well as their characteristic interaction behaviours and how these behaviours relate to learning. this information is then used to classify new learners, withthe long term goal of providing adaptive interaction support when behaviours detrimental to learning are detected. In previous research, we described a proof-of-concept version of this user modeling approach, based on unsupervised clustering and class association rules. In this paper, we describe and evaluate an improved version, implemented in a comprehensive user-modeling framework that streamlines the application of the various phases of the modeling process.
Technology integration is currently considered to be one of the most prominent challenges in the field of education, with a global trend towards integrating technology into various aspects of education. elearning is c...
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the generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of t...
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Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulations in cognitive computing and machinelearning. CA provides a rigorous and dynamic knowledge modeling an...
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