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3,422
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 561 (20 self)
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given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including supportvector machines, AdaBoost, regression, logistic regression and decisiontree algorithms. We also give a multiclass
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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of equations in the dual space. While the SVM classifier has a large margin interpretation, the LSSVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization
An experimental comparison of three methods for constructing ensembles of decision trees
 Bagging, boosting, and randomization. Machine Learning
, 2000
"... Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approac ..."
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Cited by 610 (6 self)
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of the decisiontree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than
PrivacyPreserving Data Mining
, 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
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Cited by 844 (3 self)
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without access to precise information in individual data records? We consider the concrete case of building a decisiontree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records
Solving multiclass learning problems via errorcorrecting output codes
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1995
"... Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass l ..."
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Cited by 726 (8 self)
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learning problems include direct application of multiclass algorithms such as the decisiontree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed
K.B.: MultiInterval Discretization of ContinuousValued Attributes for Classication Learning. In:
 IJCAI.
, 1993
"... Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuousvalued a ..."
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Cited by 832 (7 self)
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formally derive a criterion based on the minimum description length principle for deciding the partitioning of intervals. We demonstrate via empirical evaluation on several realworld data sets that better decision trees are obtained using the new multiinterval algorithm.
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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learning problems. We performed two sets of experiments. The first set compared boosting to Breiman’s “bagging ” method when used to aggregate various classifiers (including decision trees and single attributevalue tests). We compared the performance of the two methods on a collection of machine
Activity recognition from userannotated acceleration data
, 2004
"... In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small biaxial accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from 20 subjects without researcher supervision or observation. Subjects ..."
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Cited by 515 (7 self)
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. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequencydomain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. Decision tree classifiers showed the best
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
 Machine Learning,
, 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
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Cited by 707 (2 self)
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variants in conjunction with a decision tree inducer (three variants) and a NaiveBayes inducer. The purpose of the study is to improve our understanding of why and when these algorithms, which use perturbation, reweighting, and combination techniques, affect classification error. We provide a bias
Better prediction of protein cellular localization sites with the k nearest neighbors classifier
, 1997
"... We have compared four classifiers on the problem of predicting the cellular localization sites of proteins in yeast and E.coli. A set of sequence derived features, such as regions of high hydrophobicity, were used for each classifier. The methods compared were a structured probabilistic model specif ..."
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Cited by 142 (5 self)
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specifically designed for the localization problem, the k nearest neighbors classitier, the binary decision tree classifier, and the naive Bayes classifier. The result of tests using stratified cross validation shows the k nearest neighbors classifier to perform better than the other methods. In the case
Results 1  10
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