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boosting automatic event extraction from the literature using domain adaptation and coreference resolution

BIOINFORMATICS 2012年第13期28卷 1759-1765页

作者： Miwa, Makoto Thompson, Paul Ananiadou, Sophia Univ Manchester Natl Ctr Text Min NaCTeM Manchester M1 7DN Lancs England Univ Manchester Sch Comp Sci Manchester M1 7DN Lancs England

Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage. Results: We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task.

关键词： boosting algorithms FEATURE extraction (Data processing) BIOINFORMATICS PERFORMANCE evaluation ADAPTATION (Biology) COMMUNICABLE diseases

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A Generic Framework for the Design of Visual-based Gesture Control Interface

A Generic Framework for the Design of Visual-based Gesture C...

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5th IEEE Conference on Industrial Electronics and Applications

作者： Chen, Wen-Hui Lin, Yu-Hong Yang, Shun-Jie Natl Taipei Univ Technol Inst Automat Technol Taipei Taiwan

ISBN: (纸本)9781424450466

Most visual-based gesture control systems are bound to specific applications. They used predefined postures for users to control devices. Users need to learn and be familiar with those predefined postures to issue a command. This makes it difficult to transfer one gesture control interface into different applications. This study presented a generic framework for the design of a human machine interface based on visual-based hand gesture recognition techniques. Experimental results showed that the proposed approach is feasible for practical applications.

关键词： Human machine interface boosting algorithms Finite state machines Gesture control systems

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Evidence contrary to the statistical view of boosting

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JOURNAL OF MACHINE LEARNING RESEARCH 2008年第2期9卷 131-156页

作者： Mease, David Wyner, Abraham San Jose State Univ Coll Business Dept Marketing & Decis Sci San Jose CA 95192 USA Univ Penn Wharton Sch Dept Stat Philadelphia PA 19104 USA

The statistical perspective on boosting algorithms focuses on optimization, drawing parallels with maximum likelihood estimation for logistic regression. In this paper we present empirical evidence that raises questions about this view. Although the statistical perspective provides a theoretical framework within which it is possible to derive theorems and create new algorithms in general contexts, we show that there remain many unanswered important questions. Furthermore, we provide examples that reveal crucial flaws in the many practical suggestions and new methods that are derived from the statistical view. We perform carefully designed experiments using simple simulation models to illustrate some of these flaws and their practical consequences.

关键词： boosting algorithms LogitBoost AdaBoost

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Improvements of image-steganalysis using boosted combinatorial classifiers and Gaussian High Pass Filtering

Improvements of image-steganalysis using boosted combinatori...

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4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Asadi, Nima Jamzad, Mansour Sajedi, Hedieh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9780769532783

Powerful universal steganalyzers were proposed in the literature during the past few years. In addition some studies have been conducted on improvements of current steganalysis results using information fusion techniques, merging available feature vectors, etc. This paper presents two independent ideas, which can be used together, to obtain higher accuracy in detecting stego images. First, we propose the use of boosted fusion methods to aggregate outputs of multiple steganalyzers. Second, we investigate how passing high frequencies through filtering can enhance the results of steganalysis techniques. In this work, it is shown that, through different tests over the state-of-the-art steganography algorithms, an increase in detection accuracy is achieved by using the proposed new ideas.

关键词： steganalysis steganography boosting algorithms Gaussian filtering fusion techniques

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Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2004年第3期12卷 296-308页

作者： del Jesus, MJ Hoffmann, F Navascués, LJ Sánchez, L Univ Jaen Dept Comp Sci Jaen Spain Univ Dortmund D-44227 Dortmund Germany Univ Oviedo Dept Comp Sci E-33204 Oviedo Spain

This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. Connections between iterative learning and boosting are analyzed in terms of their respective structures and the manner these algorithms address the cooperation-competition problem. The results are used to explain some properties of the former method. The evolutionary boosting scheme is applied to approximate and descriptive fuzzy-rule bases. The advantages of boosting fuzzy rules are assessed by performance comparisons between the proposed method and other classification schemes applied on a set of benchmark classification tasks.

关键词： boosting algorithms evolutionary algorithms fuzzy-rule-based classifiers iterative learning

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Boosted classification trees and class probability/quantile estimation

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JOURNAL OF MACHINE LEARNING RESEARCH 2007年第3期8卷 409-439页

作者： Mease, David Wyner, Abraham J. Buja, Andreas San Jose State Univ Dept Mkt & Decis Sci San Jose CA 95192 USA Univ Penn Wharton Sch Dept Stat Philadelphia PA 19104 USA

The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class probability function P [ y = 1 vertical bar x]. Boosted classification trees are known to perform quite well for such problems. In this article we consider the use of standard, off-the-shelf boosting for two more general problems: 1) classification with unequal costs or, equivalently, classification at quantiles other than 1/2, and 2) estimation of the conditional class probability function P [ y = 1 vertical bar x]. We first examine whether the latter problem, estimation of P [ y = 1 vertical bar x], can be solved with Logit-Boost, and with AdaBoost when combined with a natural link function. The answer is negative: both approaches are often ineffective because they overfit P [ y = 1 vertical bar x] even though they perform well as classifiers. A major negative point of the present article is the disconnect between class probability estimation and classification. Next we consider the practice of over/under-sampling of the two classes. We present an algorithm that uses AdaBoost in conjunction with Over/Under-Sampling and Jittering of the data ("JOUS-Boost"). This algorithm is simple, yet successful, and it preserves the advantage of relative protection against overfitting, but for arbitrary misclassification costs and, equivalently, arbitrary quantile boundaries. We then use collections of classifiers obtained from a grid of quantiles to form estimators of class probabilities. The estimates of the class probabilities compare favorably to those obtained by a variety of methods across both simulated and real data sets.

关键词： boosting algorithms LogitBoost AdaBoost class probability estimation over-sampling under-sampling stratification data jittering

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A fast genetic method for inducting descriptive fuzzy models

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FUZZY SETS AND SYSTEMS 2004年第1期141卷 33-46页

作者： Sánchez, L Otero, J Univ Oviedo Dept Informat Gijon 33203 Spain

Under certain inference mechanisms, fuzzy rule bases can be regarded as extended additive models. This relationship can be applied to extend some statistical techniques to learn fuzzy models from data. The interest in this parallelism is twofold: theoretical and practical. First, extended additive models can be estimated by means of the matching pursuit algorithm, which has been related to Support Vector Machines, boosting and Radial Basis neural networks learning;this connection can be exploited to better understand the learning of fuzzy models. In particular, the technique we propose here can be regarded as the counterpart to boosting fuzzy classifiers in the field of fuzzy modeling. Second, since matching pursuit is very efficient in time, we can expect to obtain faster algorithms to learn fuzzy rules from data. We show that the combination of a genetic algorithm and the backfitting process learns faster than ad hoc methods in certain datasets. (C) 2003 Elsevier B.V. All rights reserved.

关键词： descriptive fuzzy models genetic fuzzy systems boosting algorithms backfitting matching pursuit

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Improved boosting algorithms using confidence-rated predictions

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MACHINE LEARNING 1999年第3期37卷 297-336页

作者： Schapire, RE Singer, Y AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA

We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. This method also suggests a technique for growing decision trees which turns out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multi-label case in which each example may belong to more than one class. We give two boosting methods for this problem, plus a third method based on output coding. One of these leads to a new method for handling the single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.

关键词： boosting algorithms multiclass classification output coding decision trees

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BoosTexter: A boosting-based system for text categorization

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MACHINE LEARNING 2000年第2-3期39卷 135-168页

作者： Schapire, RE Singer, Y AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.

关键词： text and speech categorization multiclass classification problems boosting algorithms

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Support vector machines for spam categorization

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1999年第5期10卷 1048-1054页

作者： Drucker, H Wu, DH Vapnik, VN AT&T Bell Labs Res Red Bank NJ 07701 USA Monmouth Univ Dept Elect Engn W Long Branch NJ 07764 USA Rensselaer Polytech Inst Troy NY 12181 USA

We study the use of support vector machines (SVM's) In classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees, These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000, SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.

关键词： boosting algorithms classification e-mail feature representation Ripper Rocchio support vector machines

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