17079123
Applying CLIR Techniques to Event Tracking
Cross-lingual event tracking from a very large number of information sources (thousands of Web sites, for example) is an open challenge. In this paper we investigate effective and scalable solutions for this problem, focusing on the use of cross-lingual information retrieval techniques to translate a small subset of the training documents, as an alternative to the conventional approach of translating all the multilingual test documents. In addition, we present a new variant of weighted pseudo-relevance feedback for adaptive event tracking. This new method simplifies the assumption and the computation in the best-known approach of this kind, yielding a better result than the latter on benchmark datasets in our evaluations.
10555290
From Lasso regression to Feature vector machine
Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optimization problems. This paper proposes a new approach named the Feature Vector Machine (FVM). It reformulates the standard Lasso regression into a form isomorphic to SVM, and this form can be easily extended for feature selection with non-linear models by introducing kernels defined on feature vectors. FVM generates sparse solutions in the nonlinear feature space and it is much more tractable compared to feature scaling kernel machines. Our experiments with FVM on simulated data show encouraging results in identifying the small number of dominating features that are non-linearly correlated to the response, a task the standard Lasso fails to complete.
1743726
Hypertext Categorization using Hyperlink Patterns and Meta Data
Hypertext poses new text classi cation research challenges as hyperlinks, content of linked documents, and meta data about related web sites all provide richer sources of information for hypertext classi cation that are not available in traditional text classi cation. We investigate the use of such information for representing web sites, and the e ectiveness of di erent classi ers (Naive Bayes, Nearest Neighbor, and Foil) in exploiting those representations. We nd that using words in web pages alone often yields suboptimal performance of classi ers, compared to exploiting additional sources of information beyond document content. On the other hand, we also observe that linked pages can be more harmful than helpful when the linked neighborhoods are highly \noisy" and that links have to be used in a careful manner. More importantly, our investigation suggests that meta data which is often available, or can be acquired using Information Extraction techniques, can be extremely useful for improving classi cation accuracy. Finally, the relative performance of the di erent classi ers being tested gives us insights into the strengths and limitations of our algorithms for hypertext classi cation.
7098682
High-performing feature selection for text classification
This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.
14181364
Probabilistic score estimation with piecewise logistic regression
Well-calibrated probabilities are necessary in many applications like probabilistic frameworks or cost-sensitive tasks. Based on previous success of asymmetric Laplace method in calibrating text classifiers' scores, we propose to use piecewise logistic regression, which is a simple extension of standard logistic regression, as an alternative method in the discriminative family. We show that both methods have the flexibility to be piecewise linear functions in log-odds, but they are based on quite different assumptions. We evaluated asymmetric Laplace method, piecewise logistic regression and standard logistic regression over standard text categorization collections (Reuters-21578 and TRECAP) with three classifiers (SVM, Naive Bayes and Logistic Regression Classifier), and observed that piecewise logistic regression performs significantly better than the other two methods in the log-loss metric.
8555900
Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10 K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
207726294
Evolving better population distribution and exploration in evolutionary multi-objective optimization
The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimization ability of multi-objective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is an enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. The strategy also preserves non-dominated solutions in the evolving population to achieve a good convergence for the optimization. Comparative studies of some well-known diversity operators, mutation operators and multi-objective evolutionary algorithms are performed on different benchmark problems, which illustrate the effectiveness and efficiency of the proposed features.
11870431
Real-Time Cooperative Editing on the Internet
New research and a prototype implementation solve some fundamental problems in distributed, real-time, cooperative editing in the Internet environment. The REDUCE system's strategies include a novel consistency model and responsiveness techniques that permit arbitrary order in executing independent operations.
43601007
Proportional k-Interval Discretization for Naive-Bayes Classifiers
This paper argues that two commonly-used discretization approaches, fixed k-interval discretization and entropy-based discretization have sub-optimal characteristics for naive-Bayes classification. This analysis leads to a new discretization method, Proportional k-Interval Discretization (PKID), which adjusts the number and size of discretized intervals to the number of training instances, thus seeks an appropriate trade-off between the bias and variance of the probability estimation for naive-Bayes classifiers. We justify PKID in theory, as well as test it on a wide cross-section of datasets. Our experimental results suggest that in comparison to its alternatives, PKID provides naive-Bayes classifiers competitive classification performance for smaller datasets and better classification performance for larger datasets.
4829760
Exploiting redundancy to implement multiobjective behavior
Teams of robots can be redundant with respect to a given task. This redundancy can be exploited to pursue additional objectives during the execution of the task. In this paper, we describe a control-based method to exploit such redundancy for the execution of additional behavior, leading to the improvement of overall performance. The control-based method provides a suitable mechanism for combining controllers with different objectives. The mechanism ensures that the subordinate controllers do not interfere with the superior controllers. Thus it allows to build controllers exhibiting complex behavior from simple primitives, while maintaining their provable performance characteristics. The effectiveness of the framework is demonstrated by experiments with a multi-robot exploration task.
2474873
A support vector machine approach for detection of microcalcifications
We investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.