Ensemble methods have been known to improve prediction accuracy over the base learning algorithm. AdaBoost is well-recognized for that in its class. However, it is susceptible to overfitting the training instances corrupted by class label noise. This paper proposes a modification to AdaBoost that is more tolerant to class label noise, which further enhances its ability to boost prediction accuracy. In particular, we observe that in Adaboost, the weight-hike of noisy examples can be constrained by careful application of a cut-off in their weights. Effectiveness of our algorithm is demonstrated empirically using some artificially generated data. We also corroborate this on a number of data sets from UCI repository (Blake and Mertz, 1998). In both experimental settings, the results obtained affirm the efficacy of our approach. Finally, some of the significant characteristics of our technique related to noisy environments have been investigated.

Learning from imbalanced data occurs very frequently in functional genomic applications. One positive example to thousands of negative instances is common in scientific applications. Unfortunately, traditional machine learning treats the extremely small instances as noise. The standard approach for this difficulty is balancing training data by resampling them. However, this results in high false positive predictions. Hence, we propose preprocessing majority instances by partitioning them into clusters. This greatly reduces the ambiguity between minority instances and instances in each cluster. For moderately high imbalance ratio and low in-class complexity, our technique gives better prediction accuracy than undersampling method. For extreme imbalance ratio like splice site prediction problem, we demonstrate that this technique serves as a good filter with almost perfect recall that reduces the amount of imbalance so that traditional classification techniques can be deployed and yield significant improvements over previous predictor. We also show that the technique works for sub cellular localization and post-translational modification site prediction problems.

Article An apprentice learning model (extended abstract) Share on Author: Stephen S. Kwek School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA School of Electrical Engineering and Computer Science, Washington State University, Pullman, WAView Profile Authors Info & Claims COLT '99: Proceedings of the twelfth annual conference on Computational learning theoryJuly 1999 Pages 63–74https://doi.org/10.1145/307400.307413Online:06 July 1999Publication History 0citation315DownloadsMetricsTotal Citations0Total Downloads315Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A novel method for problem decomposition and for local model selection in a multimodel prediction system is proposed. The proposed method partitions the data into disjoint subsets obtained by the local regression modeling and then it learns the distributions on these sets in order to identify the most appropriate regression model for each test point. The system is applied to a site specific agriculture domain and is shown to provide a substantial improvement in the prediction quality as compared to a global model. Also, some aspects of local learner choice and setting of their parameters are discussed and an overall ability of the proposed model to accurately perform regression is assessed.

) Sally A. Goldman Dept. of Computer Science Washington University St. Louis, MO 63130-4899 [email protected] Stephen S. Kwek Dept. of Computer Science Washington University St. Louis, MO 63130-4899 [email protected] Stephen D. Scott Dept. of Computer Science Washington University St. Louis, MO 63130-4899 [email protected] Abstract We introduce the UAV learning model in which some of the attributes in the examples are unspecified. In our model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is "?" (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect. We show that any class learnable in the exact model using the...

Goldberg,Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class.We present an on-line agnostic learning algorithm for learning the class of one-dimensional geometric patterns.Since, when moving from the processed visual image to a one-dimensional pattern some key information is lost, we define a class of two-dimensional geometric patterns for which the important features from the visual image are incorporated in the geometric pattern, and show how to extend our agnostic learning algorithm to this class of two-dimensional patterns.Prnllissiofl lo InnLc digltnl!l~nrd copies ofall or p;m ofllli\ tll;~ir'r~i~l lb, penonnl or cl~wsroom wsc is ~I-mli'd w~tlwut I>c prob &d th;~r 111~ cop<\ arc IMM made or distribukd IOr profit or commcrc~~~l .~tl\x~lt;~gc.tile copyrlglit nolice.Ihe liilc oflhc pul~lic;llio~l and iLv J;kIc ;nppc;kr.:III~ llollcc jl: giwn that copyrighl is hy pmn~~on ofthe ,\c'kl.IIK '1'0 copy OLIIC~W~?.~'. 10 rrpuhlirh.10 posl 01) wvm or lo rtxilstrihulc IO 11~15.rqutrcs hpcClliC pemlission and/or Ibe ( 'OL.7' 97 Naslnille.Tcnncsec.USA Copyright I997 i\Ckl O-R9791 -89 I -(v97'7..$? 50

We present algorithms for learning neural networks where the hidden depth

Article Free Access Share on PAC learning intersections of halfspaces with membership queries (extended abstract) Authors: Stephen Kwek Computer Science Department, University of Illinois, Urbana, IL Computer Science Department, University of Illinois, Urbana, ILView Profile , Leonard Pitt Computer Science Department, University of Illinois, Urbana, IL Computer Science Department, University of Illinois, Urbana, ILView Profile Authors Info & Claims COLT '96: Proceedings of the ninth annual conference on Computational learning theoryJanuary 1996 Pages 244–254https://doi.org/10.1145/238061.238109Published:01 January 1996Publication History 0citation219DownloadsMetricsTotal Citations0Total Downloads219Last 12 Months30Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF