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.
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.
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.
3009196
Translingual Information Retrieval: Learning from Bilingual Corpora
Abstract Translingual information retrieval (TLIR) consists of providing a query in one language and searching document collections in one or more different languages. This paper introduces new TLIR methods and reports on comparative TLIR experiments with these new methods and with previously reported ones in a realistic setting. Methods fall into two categories: query translation and statistical-IR approaches establishing translingual associations. The results show that using bilingual corpora for automated extraction of term equivalences in context outperforms dictionarybased methods. Translingual versions of the Generalized Vector Space Model (GVSM) and Latent Semantic Indexing (LSI) perform well, as does translingual pseudo-relevance feedback (PRF) and Example-Based Term-in-context translation (EBT). All showed relatively small performance loss between monolingual and translingual versions, ranging between 87–101% of monolingual IR performance. Query translation based on a general machine-readable bilingual dictionary—heretofore the most popular method—did not match the performance of other, more sophisticated methods. Also, the previous very high LSI results in the literature based on “mate-finding” were superseded by more realistic relevance-based evaluations; LSI performance proved comparable to that of other statistical corpus-based methods.
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.
4332805
Stable egress route selection for interdomain traffic engineering: model and analysis
We present a general model of interdomain route selection to study interdomain traffic engineering. In this model, the routing of multiple destinations can be coordinated. Thus the model can capture general traffic engineering behaviors such as load balancing and link capacity constraints. We first identify potential routing instability and inefficiency of interdomain traffic engineering. We then derive a sufficient condition to guarantee convergence. We also show that the constraints on local policies imposed by business considerations in the Internet can guarantee stability without global coordination. Using realistic Internet topology, we evaluate the extent to which routing instability of interdomain traffic engineering can happen when the constraints are violated
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.
2153539
Prediction of Protein Subcellular Multi-locations with a Min-Max Modular Support Vector Machine
How to predict subcellular multi-locations of proteins with machine learning techniques is a challenging problem in computational biology community. Regarding the protein multi-location problem as a multi-label pattern classification problem, we propose a new predicting method for dealing with the protein subcellular localization problem in this paper. Two key points of the proposed method are to divide a seriously unbalanced multi-location problem into a number of more balanced two-class subproblems by using the part-versus-part task decomposition approach, and learn all of the subproblems by using the min-max modular support vector machine (M3-SVM). To evaluate the effectiveness of the proposed method, we perform experiments on yeast protein data set by using two kinds of task decomposition strategies and three kinds of feature extraction methods. The experimental results demonstrate that our method achieves the highest prediction accuracy, which is much better than that obtained by the existing approach based on the traditional support vector machine.
17411004
Parameter tuning for induction-algorithm-oriented feature elimination
Feature selection has long been an active research topic in machine learning. Beginning with an empty set of features, it selects features most necessary for learning a target concept. Feature elimination, a newer technique, starts out with a full set of features and eliminates those most unnecessary for learning the target concept. Feature elimination tends to be more effective, can capture interacting features more easily, and suffers less from feature interaction than feature selection. Because the most unnecessary features are eliminated from the beginning, they will not mislead the induction process in terms of efficiency or accuracy. Induction-algorithm-oriented feature elimination, with particular parameter configurations, can achieve higher predictive accuracy than existing popular feature selection approaches. We propose two sets of well-tuned parameters based on empirical analysis. To understand how to achieve the best performance possible from IAOFE, we conducted a comprehensive analysis of IAOFE parameter tuning.
38911992
Effective integration of multiple tools in a loosely-coupled tool interfacing paradigm for CASE