P. Turney. Types of cost in inductive concept learning. In Proc. Workshop on Cost-Sensitive Learning at the 17th International Conference on Machine Learning (WCSL at ICML-2000. Home/Search Document Details and Download
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An Evolutionary Algorithm for Cost-Sensitive - Decision Rule Learning (2001) (Correct)
....5 an experimental evaluation on several real life datasets and a short discussion of the results are presented. The last section contains our conclusions and the directions of the future work. 2 Related work on cost sensitive learning The process of inductive learning may involve di erent costs [21] e.g. costs of tests (features) costs of cases, costs of errors. In the literature the latter kind of costs is the most commonly discussed one. Several attempts to incorporate misclassi cation costs into decision tree or decision rule learning were made so far. The rst approach was introduced ....
Turney, P.: Types of cost in inductive concept learning. In Proc. of ICML'2000 Workshop on Cost-Sensitive Learning. Stanford, CA (2000).
Budgeted Learning, Part I: The Multi-Armed Bandit Case - Omid Madani Daniel (Correct)
....specified and values in the case of PAC learners [Val84] We, however, have a firm total budget, specified before the learning begins. Budgeted learning falls under the framework of bounded rationality (e.g. RS95] and is an instance of active learning and cost sensitive learning (e.g. [Ang92, CAL94, Tur00, GGR02]) Feature costs in [Tur00, GGR02] refer to costs occuring at classification time, while we are concerned with cost during the learning phase. In typical poolbased active learning, the learner knows the feature values but not the label of the instances in the pool. Our problem is closer to unknown ....
....[Val84] We, however, have a firm total budget, specified before the learning begins. Budgeted learning falls under the framework of bounded rationality (e.g. RS95] and is an instance of active learning and cost sensitive learning (e.g. Ang92, CAL94, Tur00, GGR02] Feature costs in [Tur00, GGR02] refer to costs occuring at classification time, while we are concerned with cost during the learning phase. In typical poolbased active learning, the learner knows the feature values but not the label of the instances in the pool. Our problem is closer to unknown feature values. Similar to active ....
P. Turney. Types of cost in inductive concept learning. In Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning, pages 15--21, 2000.
Budgeted Learning, Part II: The Naïve-Bayes Case - Lizotte, Madani, Greiner (Correct)
....of ineciency and insigni cant gains (speci cally see [LMRar] However, we observe that in our case the greedy method often has poor performance, and that looking deeper can pay signi cant dividends. Budgeted learning is also related to cost sensitive learning and active classi cation (e.g. [Ang92, Tur00, GGR02]) although feature costs in [Tur00, GGR02] refer to costs at classi cation time, while we are concerned with cost during the learning phase. Nonethe Xn H H Hj Figure 1: Na ve Bayes Structure less, like several active learning results[LMRar, ....
....see [LMRar] However, we observe that in our case the greedy method often has poor performance, and that looking deeper can pay signi cant dividends. Budgeted learning is also related to cost sensitive learning and active classi cation (e.g. Ang92, Tur00, GGR02] although feature costs in [Tur00, GGR02] refer to costs at classi cation time, while we are concerned with cost during the learning phase. Nonethe Xn H H Hj Figure 1: Na ve Bayes Structure less, like several active learning results[LMRar, TK00, RM01] we show that selective querying ....
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P. Turney. Types of cost in inductive concept learning. In Workshop on CostSensitive Learning at the Seventeenth International Conference on Machine Learning, pages 15-21, 2000.
Benefit Maximizing Classification Using Feature Intervals - Ikizler (2002) (Correct)
....the minimal cost, cost sensitive learning systems may need to trade off some of the predictive accuracy and are subject to make more mistakes in quantity. 2.2. 1 Types of Cost in Supervised Learning Turney has created a taxonomy of the different types of cost in inductive concept learning in [46]. According to this taxonomy there are nine major types of costs. Some of these types can be overviewed as follows: Cost of misclassification errors: This type of errors is the most crucial one and most of the cost sensitive learning research has investigated the ways to manipulate such ....
....on individual cases. Cost of computation: Size and structural complexity, time and space requirements of a classification algorithm both in training and test phases are considered under this category. Cost of cases: Turney states that there may also be a cost of acquiring instances [46]. In such situations, it is argued that cost of cases for a batch learner and an incremental learner should be evaluated separately. In addition to these types, there may be other kind of costs such as intervention costs, unwanted achievement costs, human computer interaction costs and costs ....
[Article contains additional citation context not shown here]
P. Turney. Types of cost in inductive concept learning. In Workshop on CostSensitive Learning at the Seventeenth International Conference on Machine Learning (WCSL at ICML-2000.
Active Learning for Class Probability Estimation and Ranking - Saar-Tsechansky, Provost (2001) (1 citation) (Correct)
....example, in target marketing the estimated probability that a customer will respond to an offer is combined with the estimated profit (produced with a different model) Zadrozny and Elkan, 2001] Other applications require ranking of cases, to add flexibility to user processing. 1 We agree with Turney [Turney, 2000] that machine learning systems should be able to take into account various cost benefit information, including decision making costs as well as labeling costs. Figure 1: Learning curves for the Car data set In this paper, we consider active learning to produce accurate CPEs and class based ....
Turney, P.D. Types of cost in inductive concept learning, Workshop on Cost-Sensitive Learning at ICML2000, Stanford University, California, 15-21.
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P. Turney. Types of cost in inductive concept learning. In Proc. Workshop on Cost-Sensitive Learning at the 17th International Conference on Machine Learning (WCSL at ICML-2000.
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Peter Turney. Types of cost in inductive concept learning. In Proc. of Workshop on CostSensitive Learning at ICML2000, pages 15--21, 2000.
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P. D. Turney. Types of cost in inductive concept learning. In Proc. of the Workshop on Cost-Sensitive Learning at the 17th Intl. Conf. on Machine Learning, Palo Alto, CA, 2000.
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P. D. Turney. Types of cost in inductive concept learning. In Workshop on Cost-Sensitive Learning at the Seventeenth Int. Conference on Machine Learning, pages 15--21, Stanford University, CA, 2000.
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Turney, P. "Types of Cost in Inductive Concept Learning"Proceedings Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning (WCSL at ICML-2000), 15- 21,2000.
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