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106
Mining highspeed data streams
, 2000
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Cited by 368 (10 self)
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Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction
, 2002
"... For large, realworld inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the data and/or the computational costs associated with learning from the data. One question of practical importance is: if n ..."
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Cited by 158 (9 self)
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For large, realworld inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the data and/or the computational costs associated with learning from the data. One question of practical importance is: if n training examples are going to be selected, in what proportion should the classes be represented? In this article we analyze the relationship between the marginal class distribution of training data and the performance of classification trees induced from these data, when the size of the training set is fixed. We study twentysix data sets and, for each, determine the best class distribution for learning. Our results show that, for a fixed number of training examples, it is often possible to obtain improved classifier performance by training with a class distribution other than the naturally occurring class distribution. For example, we show that to build a classifier robust to different misclassification costs, a balanced class distribution generally performs quite well. We also describe and evaluate a budgetsensitive progressivesampling algorithm that selects training examples such that the resulting training set has a good (nearoptimal) class distribution for learning.
Types of cost in inductive concept learning
 In Workshop on CostSensitive Learning at the Seventeenth International Conference on Machine Learning
, 2000
"... Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In realworld applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted ..."
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Cited by 121 (0 self)
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Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In realworld applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on costsensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth. 1.
The Effect of Class Distribution on Classifier Learning: An Empirical Study
, 2001
"... In this article we analyze the effect of class distribution on classifier learning. We begin by describing the different ways in which class distribution affects learning and how it affects the evaluation of learned classifiers. We then present the results of two comprehensive experimental studie ..."
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Cited by 97 (2 self)
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In this article we analyze the effect of class distribution on classifier learning. We begin by describing the different ways in which class distribution affects learning and how it affects the evaluation of learned classifiers. We then present the results of two comprehensive experimental studies. The first study compares the performance of classifiers generated from unbalanced data sets with the performance of classifiers generated from balanced versions of the same data sets. This comparison allows us to isolate and quantify the effect that the training set's class distribution has on learning and contrast the performance of the classifiers on the minority and majority classes. The second study assesses what distribution is "best" for training, with respect to two performance measures: classification accuracy and the area under the ROC curve (AUC). A tacit assumption behind much research on classifier induction is that the class distribution of the training data should match the "natural" distribution of the data. This study shows that the naturally occurring class distribution often is not best for learning, and often substantially better performance can be obtained by using a different class distribution. Understanding how classifier performance is affected by class distribution can help practitioners to choose training datain realworld situations the number of training examples often must be limited due to computational costs or the costs associated with procuring and preparing the data. 1.
Tree induction vs. logistic regression: A learningcurve analysis
 CEDER WORKING PAPER #IS0102, STERN SCHOOL OF BUSINESS
, 2001
"... Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership pr ..."
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Cited by 79 (17 self)
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Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership probabilities. We use a learningcurve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (1) Contrary to prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning curves cross), so conclusions about inductionalgorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective atproducing probabilitybased rankings, although apparently comparatively less so foragiven training{set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable canbecharacterized surprisingly well by a simple measure of signaltonoise ratio.
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
 In Proceedings of the Eighteenth International Conference on Machine Learning
, 2001
"... We propose to scale learning algorithms to arbitrarily large databases by the following method. First derive an upper bound for the learner's loss as a function of the number of examples used in each step of the algorithm. ..."
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Cited by 70 (3 self)
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We propose to scale learning algorithms to arbitrarily large databases by the following method. First derive an upper bound for the learner's loss as a function of the number of examples used in each step of the algorithm.
Machine Learning from Imbalanced Data Sets 101 (Extended Abstract)
"... Foster Provost New York University fprovost@stern.nyu.edu For research to progress most effectively, we first should establish common ground regarding just what is the problem that imbalanced data sets present to machine learning systems. Why and when should imbalanced data sets be problematic? W ..."
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Cited by 62 (1 self)
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Foster Provost New York University fprovost@stern.nyu.edu For research to progress most effectively, we first should establish common ground regarding just what is the problem that imbalanced data sets present to machine learning systems. Why and when should imbalanced data sets be problematic? When is the problem simply an artifact of easily rectified design choices? I will try to pick the lowhanging fruit and share them with the rest of the workshop participants. Specifically, I would like to discuss what the problem is not. I hope this will lead to a profitable discussion of what the problem indeed is, and how it might be addressed most effectively. An early stumbling block A common notion in machine learning causes the most basic problem, and indeed often has stymied both researchoriented and practical attempts to learn from imbalanced data sets. Fortunately the problem is straightforward to fix. The stumbling block is the notion that an inductive learner produces a black b...
Toward Intelligent Assistance for a Data Mining Process: An OntologyBased Approach for CostSensitive Classification
 IEEE Transactions on Knowledge and Data Engineering
, 2005
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Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
 Data Mining and Knowledge Discovery
, 1999
"... Scalability is a key requirement for any KDD and data mining algorithm, and one of the biggest research challenges is to develop methods that allow to use large amounts of data. One possible approach for dealing with huge amounts of data is to take a random sample and do data mining on it, since for ..."
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Cited by 51 (7 self)
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Scalability is a key requirement for any KDD and data mining algorithm, and one of the biggest research challenges is to develop methods that allow to use large amounts of data. One possible approach for dealing with huge amounts of data is to take a random sample and do data mining on it, since for many data mining applications approximate answers are acceptable. However, as argued by several researchers, random sampling is difficult to use due to the difficulty of determining an appropriate sample size. In this paper, we take a sequential sampling approach for solving this difficulty, and propose an adaptive sampling method that solves a general problem covering many actual problems arising in applications of discovery science. An algorithm following this method obtains examples sequentially in an online fashion, and it determines from the obtained examples whether it has already seen a large enough number of examples. Thus, sample size is notfixed a priori; instead, it adaptively depends on the situation. Due to this adaptiveness, if we are not in a worst case situation as fortunately happens in many practical applications, then we can solve the problem with a number of examples much smaller than the required in the worst case. We prove the correctness of our method and estimates its efficiency theoretically. For illustrating its usefulness, we consider one concrete example of using sampling, provide an algorithm based on our method, and show its efficiency by experimental evaluation.
Learning ensembles from bites: A scalable and accurate approach
"... Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small vo ..."
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Cited by 45 (6 self)
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Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small votes") is a promising approach for learning from massive datasets, one that can utilize the power of boosting and bagging. We propose a framework for building hundreds or thousands of such classifiers on small subsets of data in a distributed environment. Experiments show this approach is fast, accurate, and scalable.