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860
Information Extraction from HTML: Application of a General Machine Learning Approach
 In Proceedings of the Fifteenth National Conference on Artificial Intelligence
, 1998
"... Because the World Wide Web consists primarily of text, information extraction is central to any effort that would use the Web as a resource for knowledge discovery. We show how information extraction can be cast as a standard machine learning problem, and argue for the suitability of relational lear ..."
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Cited by 175 (6 self)
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Because the World Wide Web consists primarily of text, information extraction is central to any effort that would use the Web as a resource for knowledge discovery. We show how information extraction can be cast as a standard machine learning problem, and argue for the suitability of relational learning in solving it. The implementation of a generalpurpose relational learner for information extraction, SRV, is described. In contrast with earlier learning systems for information extraction, SRV makes no assumptions about document structure and the kinds of information available for use in learning extraction patterns. Instead, structural and other information is supplied as input in the form of an extensible tokenoriented feature set. We demonstrate the effectiveness of this approach by adapting SRV for use in learning extraction rules for a domain consisting of university course and research project pages sampled from the Web. Making SRV Webready only involves adding several simple...
Data Mining using MLC++: A Machine Learning Library in C++
 INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
, 1997
"... Data mining algorithmsincluding machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classificat ..."
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Cited by 173 (20 self)
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Data mining algorithmsincluding machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called MLC++ , which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. MLC ++ not only provides a workbench for such comparisons, but also provides a library of C ++ classes to aid in the development of new algorithms, especially hybrid algorithms and multistrategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers. 1 Introduction Data warehouses containing massive amounts of data have been b...
Supervised Machine Learning: A Review of Classification Techniques. Informatica 31:249–268
, 2007
"... Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels i ..."
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Cited by 173 (0 self)
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Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored. Povzetek: Podan je pregled metod strojnega učenja. 1
Separateandconquer rule learning
 Artificial Intelligence Review
, 1999
"... This paper is a survey of inductive rule learning algorithms that use a separateandconquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of ..."
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Cited by 164 (29 self)
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This paper is a survey of inductive rule learning algorithms that use a separateandconquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.
The Power of Decision Tables
 Proceedings of the European Conference on Machine Learning
, 1995
"... . We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and realworld domains containing only discre ..."
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Cited by 158 (5 self)
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. We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and realworld domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform stateoftheart algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing crossvalidation that is applicable to incremental learning algorithms including IDTM. Using incremental crossvalidation, it is possible to crossvalidate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incre...
Tree Induction for Probabilitybased Ranking
, 2002
"... Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., c ..."
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Cited by 158 (4 self)
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Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probabilitybased rankings, and by how much. In this paper we first discuss why the decisiontree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decisiontree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reducederror pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straghtforward methods for improving probabilitybased rankings. We show that using a simple, common smoothing methodthe Laplace correctionuniformly improves probabilitybased rankings. In addition, bagging substantioJly improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on classmembership probability are required.
An extension on ―statistical comparisons of classifiers over multiple data sets‖ for all pairwise comparisons
 Journal of Machine Learning Research
"... In a recently published paper in JMLR, Demˇsar (2006) recommends a set of nonparametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic ..."
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Cited by 158 (37 self)
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In a recently published paper in JMLR, Demˇsar (2006) recommends a set of nonparametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n×n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable pvalues in multiple comparison procedures.
Incremental Reduced Error Pruning
, 1994
"... This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming , most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning , is proposed that attempts to address all of these problems. Experiments show that in many noisy domains t ..."
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Cited by 152 (23 self)
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This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming , most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning , is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description. OEFAITR9409 1 Introduction Being able to deal with noisy data is a must for algorithms that are meant to learn concepts in realworld domains. Significant effort has gone into investigating the effect of noisy data on decision tree learning algorithms (see e.g. [Quinlan, 1993, Breiman et al., 1984]). Not surprisingly, noise handling methods have also entered the emerging field of Inductive Logic Programming (ILP) [Muggleton, 1992]. Linus [Lavr...
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets
 Journal of Artificial Intelligence Research
, 1997
"... This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to c ..."
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Cited by 146 (20 self)
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This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of nonzero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worstcase bounds for this structure for several models of data distribution. We empirically demonstrate that tractablysized data structures can be produced for large realworld datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its...
Discovery of frequent Datalog patterns
, 1999
"... Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of items that are found in a sufficient number of examples. The fundamental task of as ..."
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Cited by 150 (10 self)
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Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of items that are found in a sufficient number of examples. The fundamental task of association rule and frequent set discovery has been extended in various directions, allowing more useful patterns to be discovered with special purpose algorithms. We present Warmr, a general purpose inductive logic programming algorithm that addresses frequent query discovery: a very general Datalog formulation of the frequent pattern discovery problem.
Results 11  20
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860