A Scalable Noise Reduction Technique for Large Case-Based Systems

Segata, Nicola; Blanzieri, Enrico; Cunningham, Pádraig

doi:10.1007/978-3-642-02998-1_24

Cited by 19 publications

(19 citation statements)

References 26 publications

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“…To the best of our knowledge, the most recent editing proposals are MACE (Maintenance by A Committee of Experts) [62] and LSVM (Local Support Vector Machines) [30,31]. The idea behind MACE is that the strengths of one expert algorithm will compensate for the weaknesses of another.…”

Section: Related Workmentioning

confidence: 98%

“…In order to get a better insight of the actual contribution of our work, we compare-in terms of classification accuracy and case base size-our ACBR model to Local Support Vector Machines (LSVM) [30,31]. LSVM is an editing algorithm (see Section 5 for fur- Table 5 Comparison of standard CBR methods and ACBR model configurations with LSVM in terms of mean rank of accuracy (Accuracy rank ) and mean rank of case base size (CaseBase rank ).…”

Section: Performance Comparison With Lsvmmentioning

confidence: 99%

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Adaptive case-based reasoning using retention and forgetting strategies

Salamó

López-Sánchez

2011

Knowledge-Based Systems

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Section: Related Workmentioning

confidence: 98%

Section: Performance Comparison With Lsvmmentioning

confidence: 99%

Adaptive case-based reasoning using retention and forgetting strategies

Salamó

López-Sánchez

2011

Knowledge-Based Systems

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“…Patterns and association rules can also be used in the cleansing process [27]. An example of a pattern-based data cleansing algorithm is described in [45,44]. In this method, local SVM's are used to identify and remove instances that are suspected to be noise.…”

Section: Data Cleansingmentioning

confidence: 99%

Small margin ensembles can be robust to class-label noise

2015

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:Neurocomputing 160 (2015) Subsampling is used to generate bagging ensembles that are accurate and robust to class-label noise. The effect of using smaller bootstrap samples to train the base learners is to make the ensemble more diverse. As a result, the classification margins tend to decrease. In spite of having small margins, these ensembles can be robust to class-label noise. The validity of these observations is illustrated in a wide range of synthetic and real-world classification tasks. In the problems investigated, subsampling significantly outperforms standard bagging for different amounts of class-label noise. By contrast, the effectiveness of subsampling in random forest is problem dependent. In these types of ensembles the best overall accuracy is obtained when the random trees are built on bootstrap samples of the same size as the original training data. Nevertheless, subsampling becomes more effective as the amount of class-label noise increases.

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“…Other approaches use information theoretic or machine learning heuristics to remove noisy instances. Segata et al [15], for example, remove instances that are too close or on the wrong side of the decision surface generated by a support vector machine. However, filtering has the potential downside of discarding useful instances and/or too many instances.…”

Section: Related Workmentioning

confidence: 99%

Reducing the Effects of Detrimental Instances

Smith

Martinez

2014

2014 13th International Conference on Machine Learning and Applications

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Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental or not. In this paper, we 1) extend this paradigm by weighting the instances on a continuous scale and 2) present a methodology for measuring how detrimental an instance may be for inducing a model of the data. We call our method of identifying and weighting detrimental instances reduced detrimental instance learning (RDIL). We examine RDIL on a set of 54 data sets and 5 learning algorithms and compare RDIL with other weighting and filtering approaches. RDIL is especially useful for learning algorithms where every instance can affect the classification boundary and the training instances are considered individually, such as multilayer perceptrons trained with backpropagation (MLPs). Our results also suggest that a more accurate estimate of which instances are detrimental can have a significant positive impact for handling them.

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A Scalable Noise Reduction Technique for Large Case-Based Systems

Cited by 19 publications

References 26 publications

Adaptive case-based reasoning using retention and forgetting strategies

Adaptive case-based reasoning using retention and forgetting strategies

Small margin ensembles can be robust to class-label noise

Reducing the Effects of Detrimental Instances

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