Cost-sensitive, Scalable and Adaptive Learning Using Ensemble Methods : Theory and Application

ISBN-13: 9783639134421 / Angielski / Miękka / 2009 / 188 str.

Wei Fan

Opis

Prior work in inductive learning focused on genericalgorithms that sought to reduce complexity. Thus,simplifying assumptions were made. 1. All dataresides on a single processor, and residesentirely in main memory;Clearly in modern organizations today, most dataresides in a distributedarchitecture with only small portions being residentin main memory at each moment. 2. Each datum is considered equally important and uniformcosts are assumed. In real world contexts, differentexemplars frequently havevarying costs.3. All features are freely acquired with nocomputational or monetarycosts. This is unrealistic for many applications,such as medical diagnosis. Usually, the test for eachfeature consumes different costs and cannot beignored, i.e., accurate models that only takeadvantage of the most expensive features are notacceptable. 4. Model is computed on the basis of completeknowledge. A learned hypothesis willbe applied to scenarios that are completelyrepresented in the training set. Thisassumption is more often violated than satisfied.There are usually new and unknownpatterns that traditional hypotheses will eitherignore or misclassify.

O autorze

Wei Fan received his PhD in Computer Science from Columbia
University in 2001. His research
interests are in risk analysis, high performance
computing, skewed distribution, cost-sensitive, streams,
ensemble, nonparametric methods, graph mining, feature
construction and selection, sample bias, transfer
learning, commercial data mining systems.