Error reduction through learning multiple descriptions

Ali, Kamal; Pazzani, Michael J.

doi:10.1007/bf00058611

Cited by 166 publications

(140 citation statements)

References 19 publications

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“…This work is fundamentally different from other recent machine learning work on combining multiple models (Ali & Pazzani, 1996). That work combines models in order to boost performance for a fixed cost and class distribution.…”

Section: Limitations and Future Workmentioning

confidence: 98%

Untitled

2001

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Abstract. In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.

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Section: Limitations and Future Workmentioning

confidence: 98%

Untitled

2001

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“…Diversity is a desirable property of an ensemble of classifiers, (Ali & Pazzani, 1996;Tumer & Ghosh, 1995). One metric of diversity in an ensemble of classifiers is the error correlation.…”

Section: Measuring Diversitymentioning

confidence: 99%

On Data and Algorithms: Understanding Inductive Performance

2004

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Abstract. In this paper we address two symmetrical issues, the discovery of similarities among classification algorithms, and among datasets. Both on the basis of error measures, which we use to define the error correlation between two algorithms, and determine the relative performance of a list of algorithms. We use the first to discover similarities between learners, and both of them to discover similarities between datasets. The latter sketch maps on the dataset space. Regions within each map exhibit specific patterns of error correlation or relative performance. To acquire an understanding of the factors determining these regions we describe them using simple characteristics of the datasets. Descriptions of each region are given in terms of the distributions of dataset characteristics within it.

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“…Previous work in the field has demonstrated that a combined classifier often outperforms all individual systems involved [1].…”

Section: Classifiers Combinationmentioning

confidence: 99%

Performance Analysis of a Part of Speech Tagging Task

Mihalcea

2003

Computational Linguistics and Intelligent Text Processing

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Abstract. In this paper, we attempt to make a formal analysis of the performance in automatic part of speech tagging. Lower and upper bounds in tagging precision using existing taggers or their combination are provided. Since we show that with existing taggers, automatic perfect tagging is not possible, we offer two solutions for applications requiring very high precision: (1) a solution involving minimum human intervention for a precision of over 98.7%, and (2) a combination of taggers using a memory based learning algorithm that succeeds in reducing the error rate with 11.6% with respect to the best tagger involved.

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Error reduction through learning multiple descriptions

Cited by 166 publications

References 19 publications

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On Data and Algorithms: Understanding Inductive Performance

Performance Analysis of a Part of Speech Tagging Task

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