{"title":"Supervised Novelty Detection","authors":"V. Jumutc, J. Suykens","doi":"10.1109/CIDM.2013.6597229","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597229","url":null,"abstract":"In this paper we present a novel approach and a new machine learning problem, called Supervised Novelty Detection (SND). This problem extends the One-Class Support Vector Machine setting for binary classification while keeping the nice properties of novelty detection problem at hand. To tackle this we approach binary classification from a new perspective using two different estimators and a coupled regularization term. It involves optimization over a different objective and a doubled set of Lagrange multipliers. One might consider our approach as a joint estimation of the support for different probability distributions per class where an ultimate goal is to separate classes with the largest possible angle between the normal vectors to the decision hyperplanes in the feature space. Regarding an obvious novelty of our problem we report and compare the results along the lines of standard C-SVM, LS-SVM and One-Class SVM. Experiments have demonstrated promising results that validate the usefulness of the proposed method.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127683815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Will Landecker, Michael D. Thomure, L. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, S. Brumby
{"title":"Interpreting individual classifications of hierarchical networks","authors":"Will Landecker, Michael D. Thomure, L. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, S. Brumby","doi":"10.1109/CIDM.2013.6597214","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597214","url":null,"abstract":"Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known data sets.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132975562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A two-step fast algorithm for the automated discovery of declarative workflows","authors":"Claudio Di Ciccio, Massimo Mecella","doi":"10.1109/CIDM.2013.6597228","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597228","url":null,"abstract":"Declarative approaches are particularly suitable for modeling highly flexible processes. They especially apply to artful processes, i.e., rapid informal processes that are typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. This paper describes MINERful++, a two-step algorithm for an efficient discovery of constraints that constitute declarative workflow models. As a first step, a knowledge base is built, with information about temporal statistics gathered from execution traces. Then, the statistical support of constraints is computed, by querying that knowledge base. MINERful++ is fast, modular, independent of the specific formalism adopted for representing constraints, based on a probabilistic approach and capable of eliminating the redundancy of subsumed constraints.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124836227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GPU-accelerated eXtended Classifier System","authors":"Mani Abedini, M. Kirley, R. Chiong, T. Weise","doi":"10.1109/CIDM.2013.6597250","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597250","url":null,"abstract":"XCS - the extended Classifier System - combines an evolutionary algorithm with reinforcement learning to evolve a population of condition-action rules (classifiers). Typically, population-based approaches are slow and increasing the problem size (in terms of the number of features/samples) poses a real threat to the suitability of XCS for real-world applications. Thus, reducing the execution time without losing accuracy is highly desirable. Profiling of the execution of off-the-shelf XCS implementations suggests that the rule matching process is the most computational demanding step. A solution to this is parallelization, i.e., using parallel processing techniques to speed up the matching process (and thus the entire XCS learning process). There are many ways to achieve that, using Graphic Processing Units (GPUs) is one option. Originally, GPUs were designed to conduct a sequence of graphics operations in a massively parallel fashion. Today, GPUs can be used for all sorts of general purpose calculations that are normally handled by the CPU. In this paper, we propose a hybrid rule matching process using both CPU and GPU simultaneously for a maximum performance gain. Our experimental results indicate that this approach does speed up the XCS learning process, and that the GPU is the dominant powerful computing resource in the model.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123111872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}