An ontology enhanced parallel SVM for scalable spam filter training
TL;DR: Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart.
About: This article is published in Neurocomputing.The article was published on 2013-05-01 and is currently open access. It has received 46 citations till now. The article focuses on the topics: Support vector machine & Ontology (information science).
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TL;DR: CACM is really essential reading for students, it keeps tabs on the latest in computer science and is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us.
Abstract: Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.
856 citations
TL;DR: A novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification that enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss and is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.
212 citations
Cites methods from "An ontology enhanced parallel SVM f..."
...To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one....
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TL;DR: This work describes the methodology that won the ECBDL'14 big data challenge for a bioinformatics big data problem, named as ROSEFW-RF, which is based on several MapReduce approaches to balance the classes distribution through random oversampling and detect the most relevant features via an evolutionary feature weighting process.
Abstract: The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods.In this work we describe the methodology that won the ECBDL'14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems.
126 citations
Cites background from "An ontology enhanced parallel SVM f..."
...The latter aims at preserving the original semantics of the variable by choosing a subset of the original set of features....
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01 Jan 2004
TL;DR: It is argued that formulations of better linguistic-quality are beneficial for both answer extraction and HCI in the context of QA.
Abstract: In this paper, we describe our experimentations in answer formulation for question-answering (QA) systems. In the context of QA, answer formulation can serve two purposes: improving answer extraction or improving human-computer interaction (HCI). Each purpose has different precision/recall requirements. We present our experiments for both purposes and argue that formulations of better linguistic-quality are beneficial for both answer extraction and HCI.
100 citations
TL;DR: Experimental results show that, DTFS outperforms other methods: such as Chi-squre, comprehensively measure feature selection, t-test based featureselection, term frequency based information gain, two-step based hybrid feature selection method and improved term frequency inverse document frequency method on six corpuses.
Abstract: Feature selection is often used in email classification to reduce the dimensionality of the feature space. In this study, a new document frequency and term frequency combined feature selection method (DTFS) is proposed to improve the performance of email classification. Firstly, an existing optimal document frequency based feature selection method (ODFFS) and a predetermined threshold are applied to select the most discriminative features. Secondly, an existing optimal term frequency based feature selection (OTFFS) method and another predetermined threshold are applied to select more discriminative features. Finally, ODFFS and OTFFS are combined to select the remaining features. In order to improve the convergence rate of parameter optimization, a metaheuristic method, namely global best harmony oriented harmony search (GBHS), is proposed to search these optimal predetermined thresholds. Experiments with fuzzy Support Vector Machine (FSVM) and Naive Bayesian (NB) classifiers are applied on six corpuses: PU2, CSDMC2010, PU3, Lingspam, Enron-spam and Trec2007. Experimental results show that, DTFS outperforms other methods: such as Chi-squre, comprehensively measure feature selection, t-test based feature selection, term frequency based information gain, two-step based hybrid feature selection method and improved term frequency inverse document frequency method on six corpuses.
64 citations
Cites methods from "An ontology enhanced parallel SVM f..."
...Currently, much work on email classification has been done using the techniques such as decision trees (DT) [3], Naïve Bayesian classifiers [4], neural networks (NN) [5], Support Vector Machines (SVM) [6], Boosting [7], k-nearest neighbor (KNN) [8] and so on....
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References
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TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
20,196 citations
"An ontology enhanced parallel SVM f..." refers methods in this paper
...We also evaluate the effect of conventional bagging and boosting techniques on the performance of the parallel SVM. Section 6 concludes the paper and points out some future work....
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TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Abstract: More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
19,603 citations
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
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17,663 citations