15260186
Using recursive classification to discover predictive features
Finding most predictive features for statistical classification is a challenging problem and has important applications. Support Vector Machines (SVMs), for example, have been found successful with a recursive procedure in selecting most important genes for cancer prediction. It is not well understood, however, how much the success depends on the choice of the classifier, and how much on the recursive procedure. We answer this question by examining multiple classifiers (SVM, ridge regression and Rocchio) with feature selection in recursive and non-recursive settings, on a DNA microarray dataset (AMLALL) and a text categorization benchmark (Reuters-21578). We found recursive ridge regression most effective: its best classification performance (zero error) on the AMLALL dataset was obtained when using only 3 genes (selected from over 7000), which is more impressive than the best published result on the same benchmark - zero error of recursive SVM using 8 genes. On Reuters-21578, recursive ridge regression also achieves the best result ever published (the improvement was verified in a significance test). An in-depth analysis of the experimental results shows that the choice of classifier heavily influences the recursive feature selection process: the ridge regression classifier tends to penalize redundant features to a much larger extent than the SVM does.
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.
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.
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.
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.
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.
2183
Path-independent load balancing with unreliable machines
We consider algorithms for load balancing on unreliable machines. The objective is to optimize the two criteria of minimizing the makespan and minimizing job reassignments in response to machine failures. We assume that the set of jobs is known in advance but that the pattern of machine failures is unpredictable. Motivated by the requirements of BGP routing, we consider path-independent algorithms, with the property that the job assignment is completely determined by the subset of available machines and not the previous history of the assignments. We examine first the question of performance measurement of path-independent load-balancing algorithms, giving the measure of makespan and the normalized measure of reassignments cost. We then describe two classes of algorithms for optimizing these measures against an oblivious adversary for identical machines. The first, based on independent random assignments, gives expected reassignment costs within a factor of 2 of optimal and gives a makespan within a factor of O (log m/log log m) of optimal with high probability, for unknown job sizes. The second, in which jobs are first grouped into bins and at most one bin is assigned to each machine, gives constant-factor ratios on both reassignment cost and makespan, for known job sizes. Several open problems are discussed.
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.
34587947
Guaranteed Mutually Consistent Checkpointing in Distributed Computations
In this paper, we emplore the isomorphism between vector time and causality to characterize consistency of a set of checkpoints in a distributed computing. A necessary and sufficient condition, to determine if a set of checkpoints can form a consistent global checkpoint, is presented and proved using the isomorphic power of vector time and causality. To the best of our knowledge, this is the first attempt to use the isomorphism for this purpose. This condition leads to a simple and straightforward algorithm for a guaranteed mutually consistent global checkpointing. In our approach, a process can take a checkpoint whenever and wherever it wants while other related process may be asked to take an additional checkpoint for ensuring the mutual consistency. We also show how this condition and the resulting algorithm can be used to obtain a maximum and minimum global checkpoints, another important paradigm for distributed applications.
2679071
A generic operation transformation scheme for consistency maintenance in real-time cooperative editing systems
In real-time cooperative editing systems, independent operations on any part of the shared document may be generatedfrom multiple cooperatingsites. It is very important and technically challenging to ensure that the effect of executing an operation at remote sites, in the presence of concurrent execution of independent operations, achieves the same effect aa executing this operation at the local site at the time -of ita generation, thuspreaeruing its intention and maintainingsystern consistency. In this paper, we investigate the technical issues involved in preserving intentions of concurrent operuCons, explain the reaaons why traditional serialization-based concurrency control strategies and existing operational transformation strategies failed to solve these problems, and propose a generic operation transformation scheme for intention preservation and consistency maintenance in real-time cooperative editing systems. The proposed scheme has been implemented in an Internet-based prototype REDUCE (REaLtime Distributed Unconstrained Cooperative Editing) system. is quick (idealy as quick as a single-user editor) and the latency for remote user actions is low (determined by external commkkation latency only). (2) Distributed environments cooperating users may reside on different machines connected by different communication networks. (3) Unconstrained collaboration mdtiple users may concurrently and freely edit, any part of the document at any time, as advocated in [2, 8, 91.