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Efficient Progressive Sampling
, 1999
"... Having access to massiveamounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size is rarely obvious. We analyze methods for progressive samplingstarting with ..."
Abstract

Cited by 91 (9 self)
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Having access to massiveamounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size is rarely obvious. We analyze methods for progressive samplingstarting with small samples and progressively increasing them as long as model accuracy improves. We show that a simple, geometric sampling schedule is efficient in an asymptotic sense. We then explore the notion of optimal efficiency: what is the absolute best sampling schedule? We describe the issues involved in instantiating an "optimally efficient" progressive sampler. Finally,we provide empirical results comparing a variety of progressive sampling methods. We conclude that progressive sampling often is preferable to analyzing all data instances.
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 62 (16 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.
On Issues of Instance Selection

, 2002
"... The digital technologies and computer advances with the booming internet uses have led to massive data collection (corporate data, data warehouses, webs, just to name a few) and information (or misinformation) explosion. Szalay and Gray described this phenomenon as “drowning in data” (Szalay and Gra ..."
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Cited by 16 (0 self)
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The digital technologies and computer advances with the booming internet uses have led to massive data collection (corporate data, data warehouses, webs, just to name a few) and information (or misinformation) explosion. Szalay and Gray described this phenomenon as “drowning in data” (Szalay and Gray, 1999). They reported that each year the detectors at the CERN particle collider in Switzerland record 1 petabyte of data; and researchers in areas of science from astronomy to the human genome are facing the same problems and choking on information. A very natural question is “now that we have gathered so much data, what do we do with it?” Raw data is rarely of direct use and manual analysis simply cannot keep pace with the fast growth of data. Data mining and knowledge discovery (KDD), as a new emerging field comprising disciplines such as databases, statistics, machine learning, comes to the rescue. KDD attempts to turn raw data into nuggets and create special edges in this ever competitive world for science discovery and business intelligence. The KDD process is defined in Fayyad et al. (1996) as the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. Data Mining processes include data selection, preprocessing, data mining, interpretation and evaluation.
Distributed Data Mining: Scaling up and beyond
 In Advances in Distributed and Parallel Knowledge Discovery
, 1999
"... In this chapter I begin by discussing Distributed Data Mining (DDM) for scaling up, beginning by asking what scaling up means, questioning whether it is necessary, and then presenting a brief survey of what has been done to date. I then provide motivation beyond scaling up, arguing that DDM is a mor ..."
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Cited by 14 (0 self)
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In this chapter I begin by discussing Distributed Data Mining (DDM) for scaling up, beginning by asking what scaling up means, questioning whether it is necessary, and then presenting a brief survey of what has been done to date. I then provide motivation beyond scaling up, arguing that DDM is a more natural way to view data mining generally. DDM eliminates many difficulties encountered when coalescing alreadydistributed data for monolithic data mining, such as those associated with heterogeneity of data and with privacy restrictions. By viewing data mining as inherently distributed, important open research issues come into focus, issues that currently are obscured by the lack of explicit treatment of the process of producing monolithic data sets. I close with a discussion of the necessity of DDM for an efficient process of knowledge discovery.
Data Reduction Using Multiple Models Integration
"... Large amount of available information does not necessarily imply that induction algorithms must use all this information. Samples often provide the same accuracy with less computational cost. We propose several effective techniques based on the idea of progressive sampling when progressively larg ..."
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Large amount of available information does not necessarily imply that induction algorithms must use all this information. Samples often provide the same accuracy with less computational cost. We propose several effective techniques based on the idea of progressive sampling when progressively larger samples are used for training as long as model accuracy improves. Our sampling procedures combine all the models constructed on previously considered data samples. In addition to random sampling, controllable sampling based on the boosting algorithm is proposed, where the models are combined using a weighted voting. To improve model accuracy, an effective pruning technique for inaccurate models is also employed. Finally, a novel sampling procedure for spatial data domains is proposed, where the data examples are drawn not only according to the performance of previous models, but also according to the spatial correlation of data. Experiments performed on several data sets showed that the proposed sampling procedures outperformed standard progressive sampling in both the achieved accuracy and the level of data reduction.