Scope classification: An instance-based learning algorithm with a rule-based characterisation

During the training phase of the Nearest-Neighbor (NN) algorithm, considered the most popular Instance-Based Learning (IBL) algorithm, all training instances are stored as the description of the learned concept. IBL algorithms postpone the generalization process, which usually happens during the training phase, until the classification phase starts i.e., when an unclassified data instance needs to be classified. When training sets have a high volume of instances, to store them all becomes unfeasible mainly due to storage requirements. Several IBL algorithms overcome storage related problems by implementing data volume reduction i.e., by storing only a representative subset of the training set. The investigation described in this paper focuses on four IBL algorithms that implement data reduction, which have been empirically evaluated in data sets from the UCI Repository. Their performance, considering storage reduction and classification accuracy, are presented and discussed.

Instance-based learning techniques use a set of stored training instances to classify new examples. The most common such learning technique is the nearest neighbor method, in which new instances are classified according to the closest training instance. A critical element of any such method is the metric used to determine distance between instances. Euclidean distance is by far the most commonly used metric; no one, however, has systematically considered whether a different metric, such as Manhattan distance, might perform equally well on naturally occurring data sets. Some evidence from psychological research indicates that Manhattan distance might be preferable in some circumstances. This paper examines three different distance metrics and presents experimental comparisons using data from three domains: malignant cancer classification, heart disease diagnosis, and diabetes prediction. The results of these studies indicate that the Manhattan distance metric works works quite well, although not better than the Euclidean metric that has become a standard for machine learning experiments. Because the nearest neighbor technique provides a good benchmark for comparisons with other learning algorithms, the results below include a number of such comparisons, which show that nearest neighbor, using any distance metric, compares quite well to other machine learning techniques.

The main goal of Nearest Prototype Classification is to reduce storage space and retrieval time of classical Instance-Based Learning (IBL) algorithms. This motivation is higher in relational data since relational distance metrics are much more expensive to compute than classical distances like Euclidean distance. In this paper, we present an algorithm to build Relational Nearest Prototype Classifiers (RNPCs). When compared with Relational Instance-Based Learning (Relational IBL or RIBL) approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can also be bound by the user. In this work, we also show an application of RNPC for automated planning. Specifically, we describe a modeling task where a relational policy is built following an IBL approach. This approach uses the decisions taken by a planning system as learning examples. We show that when the number of learning examples is reduced with RNPC, the resulting policy is able to scale up better than the original planning system.

Case-based approaches to classification, as instance-based learning techniques, have a particular reliance on training examples that other supervised learning techniques do not have. In this paper we present the RDCL case profiling technique that categorises each case in a case-base based on its classification by the case-base, the benefit it has and/or the damage it causes by its inclusion in the case-base. We show how these case profiles can identify the cases that should be removed from a case-base in order to improve generalisation accuracy and we show what aspects of existing noise reduction algorithms contribute to good performance and what do not.

Following vascular injury, endothelial cells become activated in the attempt to repave the damaged luminal surface. In addition, a physical interaction is established not only among the denuded subendothelial matrix, activated platelets, and circulating inflammatory cells but also with the progenitor cells (PCs).1 Endothelial survival, proliferation, and migration, necessary for re-coverage of exposed lamina, are aided by CD34+ PCs that express the vascular endothelial growth factor (VEGF) receptor-2/kinase-insert domain receptor (KDR). However, whether these cells enter the peripheral circulation as a predefined population or acquire their final antigenic phenotype on homing to the peripheral vasculature remains a matter of debate. See accompanying article on page 408 In this issue of Arteriosclerosis, Thrombosis, and Vascular Biology , de Boer et al propose that human CD34+/KDR+ cells are generated from circulating CD34+ cells after immobilization on activated platelets.2 By using an ex vivo flow model, the authors show that activated platelets favor the homing of CD34+ cells to sites of vascular injury and that, on cell immobilization, KDR is rapidly translocated from an endosomal compartment to the cell surface. Presumably, PCs shed afterward to “carry the message” into the circulation. It is noteworthy that de Boer et al also report an increased coexpression of KDR on CD34+ cells in type-2 diabetes, which was reduced by aspirin treatment. The authors conclude that their data might have implications for the identification of patients with subclinical vascular injury, on the basis of the hypothesis that activated/injured endothelium provides more opportunity …

Reinforcement learning is an unsupervised learning algorithm, where learning is based upon feedback from the environment. Prior research has proposed cognitive (e.g., Instance-based Learning or IBL) and statistical (Q-learning) reinforcement learning algorithms. However, an evaluation of these algorithms in a single dynamic environment has not been explored. In this paper, a comparison between the statistical Q-learning algorithm and the cognitive IBL algorithm is presented. A well-known environment, “Frozen Lake,” is used to train, generalize, and scale Q-learning and IBL algorithms. For generalizing, the Q-learning and IBL agents were trained on one version of the Frozen Lake and tested on a permuted version of the same environment. For scaling, the two algorithms were tested on a larger version of the Frozen Lake environment. Results revealed that the IBL algorithm used less training time and generalized better to different environment variants. The IBL algorithm was also able to show scalability by retaining its superior performance in the larger environment. These results indicate that the IBL algorithm could be proposed as an alternative to the standard reinforcement learning algorithms based on dynamic programming such as Q-learning. The inclusion of human factors (such as memory) in the IBL algorithm makes it suitable for robust learning in complex and dynamic environments.

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Questionnaires to Assess Urinary and Anal Incontinence: Review and Recommendations

Article Free Access Share on Efficient repeating pattern finding in music databases Authors: Jia-Lien Hsu Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, R.O.C Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, R.O.CView Profile , Arbee L. P. Chen Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, R.O.C Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, R.O.CView Profile , C.-C. Liu Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, R.O.C Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, R.O.CView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 281–288https://doi.org/10.1145/288627.288668Online:01 November 1998Publication History 44citation667DownloadsMetricsTotal Citations44Total Downloads667Last 12 Months20Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Journal of High Resolution ChromatographyVolume 3, Issue 10 p. 521-522 Short Communication Capillary GC and GC/MS of bis(trimethylsiloxy)silicon(IV) derivatives of alkyl porphyrins R. Alexander, R. Alexander Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this authorG. Eglinton, Corresponding Author G. Eglinton Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandOrganic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this authorJ. P. Gill, J. P. Gill Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this authorJ. K. Volkman, J. K. Volkman Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this author R. Alexander, R. Alexander Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this authorG. Eglinton, Corresponding Author G. Eglinton Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandOrganic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this authorJ. P. Gill, J. P. Gill Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this authorJ. K. Volkman, J. K. Volkman Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, EnglandSearch for more papers by this author First published: October 1980 https://doi.org/10.1002/jhrc.1240031007Citations: 20AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume3, Issue10October 1980Pages 521-522 RelatedInformation

Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture interdependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction.

This paper will describe a successful engineering study abroad program that occurs every summer for six weeks. The program participants are engineering and science students from Mississippi State University and Clemson University in the United States and the program takes place at the University of Bristol in the United Kingdom. The program is designed to handle approximately 30 students in total from the two US schools and permits wavering enrollments at one university to be compensated by the other. Thus, the University of Bristol can be assured of a reasonable number of students on a continuing basis to keep the program profitable. The first four weeks are spent at the University of Bristol, where students take two classes and earn six credit hours. The program ends with a two-week independent travel period where students typically tour the continent. While attending classes at the University of Bristol, the students experience British culture firsthand by living with host families.