This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.

/pdf/text-categorization-with-suport-vector-machines-learning-1onl65lfqq.pdf

Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.

The relationship between Precision-Recall and ROC curves

This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassi cations of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classi cation. These theoretical ndings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs e ciently, handling 10,000 examples and more.

Transductive Inference for Text Classification using Support Vector Machines

While software metrics are a generally desirable feature in the software management functions of project planning and project evaluation, they are of especial importance with a new technology such as the object-oriented approach. This is due to the significant need to train software engineers in generally accepted object-oriented principles. This paper presents theoretical work that builds a suite of metrics for object-oriented design. In particular, these metrics are based upon measurement theory and are informed by the insights of experienced object-oriented software developers. The proposed metrics are formally evaluated against a widelyaccepted list of software metric evaluation criteria.

http://www.cs.sjsu.edu/faculty/pearce/cs251b/p197-chidamber.pdf

Towards a metrics suite for object oriented design

Motivation: The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug–target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy.

Results: We show that a simple machine learning method that uses the drug–target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug–target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug–target interaction networks used in previous studies. The proposed algorithm achieves area under the precision–recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug–target interactions.

Availability: Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/.

Contact:tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl

Supplementary Information:Supplementary data are available at Bioinformatics online.

/pdf/gaussian-interaction-profile-kernels-for-predicting-drug-1bmcxu7g4c.pdf

Gaussian interaction profile kernels for predicting drug–target interaction

Recall and precision are often used to evaluate the effectiveness of information retrieval systems. They are easy to define if there is a single query and if the retrieval result generated for the query is a linear ordering. However, when the retrieval results are weakly ordered, in the sense that several documents have an identical retrieval status value with respect to a query, some probabilistic notion of precision has to be introduced. Relevance probability, expected precision, and so forth, are some alternatives mentioned in the literature for this purpose. Furthermore, when many queries are to be evaluated and the retrieval results averaged over these queries, some method of interpolation of precision values at certain preselected recall levels is needed. The currently popular approaches for handling both a weak ordering and interpolation are found to be inconsistent, and the results obtained are not easy to interpret. Moreover, in cases where some alternatives are available, no comparative analysis that would facilitate the selection of a particular strategy has been provided. In this paper, we systematically investigate the various problems and issues associated with the use of recall and precision as measures of retrieval system performance. Our motivation is to provide a comparative analysis of methods available for defining precision in a probabilistic sense and to promote a better understanding of the various issues involved in retrieval performance evaluation.

A critical investigation of recall and precision as measures of retrieval system performance

Over the last decade many software metrics have been introduced by researchers and many software tools have been developed using software metrics to measure the "quality" of programs. These metrics for measuring productivity, reliability, maintainability, and complexity, for example, are vital to software development planning and management. In this paper a new approach is presented to describe the properties of the software metrics and their scales using measurement theory. Methods are shown to describe a software complexity metric as an ordinal, an interval or a ratio scale. The use of this concept is shown by application to the Metric of McCabe. These results are very important for selecting appropriate software metrics for software measurement and for developing tools which use software metrics to evaluate the "quality" of software.

Software metrics: using measurement theory to describe the properties and scales of static software complexity metrics

In this paper evaluation measures for information retrieval system outputs are investigated from a measurement theoretic point of view. Two axioms are introduced: the axiom of monoto-nicity and the Archimedian axiom. It is shown that the measures fullfilling these axioms are exactly the measures equivalent to some measure of the form αa + δd where a is the number of relevant retrieved documents and d is the number of nonrelevant not retrieved documents. Some consequences for retrieval tests are discussed.

Two axioms for evaluation measures in information retrieval

Besides the operators 'and', 'or' and 'not' the GRIPS retrieval language contains thesaurus — operators to extend the query and truncation — and context-operators for freetext and Boolean searching.In a similar way several other viewpoints and evaluation measures were defined and applied in the retrieval experiment. Under the assumption that the evaluation measure is an interval scale averaging is done by calculating the arithmetic mean.As levels of the experimental factor /8/ the following similarity measures were used: inner product measure, cosine measure, overlap measure, coefficient of Jaccard and Euclidean distance. As situative factors /8/ the number of documents retrieved by GRIPS, the number of descriptiors of the queries, generality and topic of documents were used.The retrieval experiment is not yet finished completely but several results have already been obtained. For example in the average (over 81 queries) for the above defined viewpoint the ranking with the inner product measure does not indicate a significant improvement compared with the GRIPS-output. In the case of the Euclidean distance measure it seems that in the average the user has to inspect less nonrelevant documents. This means an improvement compared with the unordered retrieved set from the GRIPS-output.For more details of the so called 'two-level retrieval process' and further experimental results we refer to the long version of this paper.

The LIVE-project: retrieval experiments based on evaluation viewpoints

Retrieval system evaluation plays an important role in judging the efficiency and effectiveness of the retrieval process. Several different evaluation criteria, deemed most critical to user population, were pointed out ir(SALTON83, CLEVERDON70]; namely, recall, precision, effort, time, form of presentation and coverage. Among them, recall and precision have received the most attention in the literature. Recall is defined as the ratio of the number of relevant documents that are retrieved to the total number of relevant documents. Preci&n, on the other hand, is the number of relevant documenrs retrieved divided by the number of retrieved documents. In particular, a recall-precision graph is often used as a combined evaluation measure of retrieval systems. Such a graph, given an arbitrary recall point, tells us the corresponding precision value.

Retrieval system evaluation using recall and precision: problems and answers

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