10010959
Robustness of regularized linear classification methods in text categorization
Real-world applications often require the classification of documents under situations of small number of features, mis-labeled documents and rare positive examples. This paper investigates the robustness of three regularized linear classification methods (SVM, ridge regression and logistic regression) under above situations. We compare these methods in terms of their loss functions and score distributions, and establish the connection between their optimization problems and generalization error bounds. Several sets of controlled experiments on the Reuters-21578 corpus are conducted to investigate the robustness of these methods. Our results show that ridge regression seems to be the most promising candidate for rare class problems.
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.
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.
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.
14899560
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
38612898
Transient behaviors of TCP-friendly congestion control protocols
We investigate the fairness, smoothness, responsiveness, and aggressiveness of TCP and three representative TCP-friendly congestion control protocols: GAIMD, TFRC, and TEAR. The properties are evaluated both analytically and via simulation by studying protocol responses to three network environment changes. The first environment change is the inherent fluctuations in a stationary network environment. Under this scenario, we consider three types of sending rate variations: smoothness, short-term fairness, and long-term fairness. For a stationary environment, we observe that smoothness and fairness are positively correlated. We derive an analytical expression for the sending rate coefficient of variation for each of the four protocols. These analytical results match well with experimental results. The other two environment changes we study are a step increase of network congestion and a step increase of available bandwidth. Protocol responses to these changes reflect their responsiveness and aggressiveness, respectively.
18309490
Structure Pruning Strategies for Min-Max Modular Network
The min-max modular network has been shown to be an efficient classifier, especially in solving large-scale and complex pattern classification problems. Despite its high modularity and parallelism, it suffers from quadratic complexity in space when a multiple-class problem is decomposed into a number of linearly separable problems. This paper proposes two new pruning methods and an integrated process to reduce the redundancy of the network and optimize the network structure. We show that our methods can prune a lot of redundant modules in comparison with the original structure while maintaining the generalization accuracy.
478127
A conceptual framework for society-oriented decision support
Inspired by the operation of human social organisation, this paper presents a new architecture—a pyramid-committee—for developing society-oriented intelligence, whose structure imitates the organisation of human society in its decision making. The system takes a pyramid-like hierarchical structure with links in the pyramid forming a semi-lattice, which relate not only to nodes in the same layer, but also to others in different layers. The output of the system is a result of the negotiation and balancing of different interests. For such a system to function, the main difficulties concern the complicated relationships between different factors or agents. Focussing on the airport environment audit, we discuss the development of a model framework and the role of neural networks.
14320102
Efficient Motion Planning Based on Disassembly
Disassembly-based motion planning (DBMP) is a novel and efficient single-query, sampling-based motion planning approach for free-flying robots. Disassembly-based motion planning uses workspace information to determine the workspace volume of a potential solution path and uses this information to exclude large portions of configuration space from exploration. It also identifies the most constrained placements of the robot along the potential solution path. These placements are referred to as assemblies because they are highly constrained by the environment, much like parts in an assembly are constrained. The constraints limit the possible motions of the robot and thus can be exploited to further limit configuration space exploration. The use of these two sources of workspace information permits the solution of many practical problems with very limited configuration space exploration. This reduction in configuration space exploration results in performance improvements of several orders of magnitude, compared to state-of-the-art motion planning methods. For non-free-flying robots, disassembly-based motion planning performs at least as well as the sampling-based motion planning method it is based on.