Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms

Aha, David W.

doi:10.1016/0020-7373(92)90018-g

Cited by 324 publications

(179 citation statements)

References 10 publications

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“…LLP has been tested with both labeling heuristics (see Sect. 3.3), for cluster sizes k ∈ [2,12]. As parameters for the evolutionary strategy, we used maxgen = 10, psize = 25, mutvar = 1.0, crossprob = 0.3 and tournsize = 0.25.…”

Section: Prediction Performance Experimentsmentioning

confidence: 99%

“…However, the cluster methods also assign labels to each observation in sample U , allowing for a subsequent training of other classifiers. Based on the clustering results, we have trained models for Naïve Bayes [14], kNN [2], Decision Trees [20], Random Forests [4], and the SVM [22] with linear and radial basis kernel. The model parameters have been optimized by a grid or evolutionary search.…”

Section: Prediction Performance Experimentsmentioning

confidence: 99%

“…Musicant et al [18] formally defined the problem of learning from aggregate values for regression and classification tasks. They modified well-known methods like k-NN [2], backpropagation neural networks [17] and the linear SVM [22] to respect the given label proportions. Their experimental results focus on regression tasks, while we are mainly interested in classification.…”

Section: Related Workmentioning

confidence: 99%

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Learning from Label Proportions by Optimizing Cluster Model Selection

Stolpe

Morik

2011

Machine Learning and Knowledge Discovery in Databases

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Abstract. In a supervised learning scenario, we learn a mapping from input to output values, based on labeled examples. Can we learn such a mapping also from groups of unlabeled observations, only knowing, for each group, the proportion of observations with a particular label? Solutions have real world applications. Here, we consider groups of steel sticks as samples in quality control. Since the steel sticks cannot be marked individually, for each group of sticks it is only known how many sticks of high (low) quality it contains. We want to predict the achieved quality for each stick before it reaches the final production station and quality control, in order to save resources. We define the problem of learning from label proportions and present a solution based on clustering. Our method empirically shows a better prediction performance than recent approaches based on probabilistic SVMs, Kernel k-Means or conditional exponential models.

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Section: Prediction Performance Experimentsmentioning

confidence: 99%

Section: Prediction Performance Experimentsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Learning from Label Proportions by Optimizing Cluster Model Selection

Stolpe

Morik

2011

Machine Learning and Knowledge Discovery in Databases

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“…The nearest neighbour algorithm is one of the most popular of CBR algorithms [14]. The formulation of this algorithm, called NN, or k-NN in the more sophisticated version is extremely simple.…”

Section: Case-based Reasoning and Support Vector Machinesmentioning

confidence: 99%

Real-Time Scheduling of Flexible Manufacturing Systems Using Support Vector Machines and Case-Based Reasoning

Priore¹,

Pino²,

Parreño³

et al. 2015

JOEBM

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Abstract-Dispatching rules are usually applied to schedule jobs in Flexible Manufacturing Systems (FMSs) dynamically. Despite their frequent use, one of the drawbacks that they display is that the state the manufacturing system is in dictates the level of performance of the rule. As no rule is better than all the other rules for all system states, it would highly desirable to know which rule is the most appropriate for each given condition, and to this end this paper proposes a scheduling approach that employs Support Vector Machines (SVMs) and case-based reasoning (CBR). Using these latter techniques, and by analysing the earlier performance of the system, "scheduling knowledge" is obtained whereby the right dispatching rule at each particular moment can be determined. A module that generates new control attributes is also designed in order to improve the "scheduling knowledge" that is obtained. Simulation results show that the proposed approach leads to significant performance improvements over existing dispatching rules.

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“…Important special cases of the above general form are the so-called Akaike Information Criterion (AIC) [Akaike 1974] and the Bayesian Information Criterion (BIC) [Schwarz 1978]. The former results for κ = 2 and is derived from asymptotic decision theoretic considerations.…”

Section: Information Criteriamentioning

confidence: 99%