Partial example acquisition in cost-sensitive learning

It is often expensive to acquire data in real-world data mining applications. Most previous data mining and machine learning research, however, assumes that a fixed set of training examples is given. In this paper, we propose an online cost-sensitive framework that allows a learner to dynamically acquire examples as it learns, and to decide the ideal number of examples needed to minimize the total cost. We also propose a new strategy for Partial Example Acquisition (PAS), in which the learner can acquire examples with a subset of attribute values to reduce the data acquisition cost. Experiments on UCI datasets show that the new PAS strategy is an effective method in reducing the total cost for data acquisition.

[1]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[2]  Russell Greiner,et al.  Budgeted learning of nailve-bayes classifiers , 2002, UAI 2002.

[3]  Foster J. Provost,et al.  Active feature-value acquisition for classifier induction , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[4]  Andrew McCallum,et al.  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..

[5]  Thomas G. Dietterich,et al.  Pruning Improves Heuristic Search for Cost-Sensitive Learning , 2002, ICML.

[6]  Peter D. Turney.  Types of Cost in Inductive Concept Learning , 2002, ArXiv.

[7]  Russell Greiner,et al.  Learning and Classifying Under Hard Budgets , 2005, ECML.

[8]  Haym Hirsh,et al.  Improving Short-Text Classification using Unlabeled Data for Classification Problems , 2000, ICML.

[9]  Kai Ming Ting,et al.  Inducing Cost-Sensitive Trees via Instance Weighting , 1998, PKDD.

[10]  Bojan Cestnik,et al.  Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.

[11]  Maytal Saar-Tsechansky,et al.  Economical active feature-value acquisition through Expected Utility estimation , 2005, UBDM '05.

[12]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[13]  Tong Zhang,et al.  The Value of Unlabeled Data for Classification Problems , 2000, ICML 2000.

[14]  Irving John Good,et al.  The Estimation of Probabilities: An Essay on Modern Bayesian Methods , 1965 .

[15]  Xindong Wu,et al.  Cost-constrained data acquisition for intelligent data preparation , 2005, IEEE Transactions on Knowledge and Data Engineering.

[16]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[17]  Peter D. Turney.  Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..

[18]  Russell Greiner,et al.  Budgeted Learning of Naive-Bayes Classifiers , 2003, UAI.

[19]  Andrew McCallum,et al.  Toward Optimal Active Learning through Sampling Estimation of Error Reduction , 2001, ICML.

[20]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[21]  Qiang Yang,et al.  Decision trees with minimal costs , 2004, ICML.

[22]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[23]  Foster J. Provost,et al.  Active Sampling for Class Probability Estimation and Ranking , 2004, Machine Learning.

[24]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[25]  Dragos D. Margineantu,et al.  Active Cost-Sensitive Learning , 2005, IJCAI.

[26]  Qiang Yang,et al.  Test-cost sensitive naive Bayes classification , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[27]  Ran El-Yaniv,et al.  Online Choice of Active Learning Algorithms , 2003, J. Mach. Learn. Res..