A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms

Wettschereck, Dietrich; Aha, David W.; Mohri, Takao

doi:10.1007/978-94-017-2053-3_11

Cited by 241 publications

(323 citation statements)

References 37 publications

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“…In the AI arena there are two main streams. The first one looks for weightings of the attributes values to be used when we measure the similarity of observed cases in the past to solve present situations (Blum & Langley, 1997;Wettschereck, Aha, & Mohri, 1997); usually, the assigned weightings range between 0 and 1. On the other hand, there are the proposals that search for a subset of relevant or not redundant attributes to be taken into account by any ML algorithm (John, Kohavi, & Pfleger, 1994); these methodologies select the more relevant attributes, removing the rest.…”

Section: Machine Learning Outputsmentioning

confidence: 99%

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

Goyache

Bahamonde

Alonso

et al. 2001

Trends in Food Science & Technology

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In this paper we advocate the application of Artificial Intelligence techniques to quality assessment of food products. Machine Learning algorithms can help us to: (a) extract operative human knowledge from a set of examples; (b) conclude interpretable rules for classifying samples regardless of the non-linearity of the human behaviour or process; and (c) help us to ascertain the degree of influence of each objective attribute of the assessed food on the final decision of an expert. We illustrate these topics with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial applications. #

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Section: Machine Learning Outputsmentioning

confidence: 99%

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

Goyache

Bahamonde

Alonso

et al. 2001

Trends in Food Science & Technology

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“…4 For the NN classifier, we used the simple Euclidean distance measure, and we didn't include feature selection or feature weighting (even though it is well-known that irrelevant features can badly deteriorate and, on the other hand, that feature weighting can greatly improve performance [32]). In fact, we didn't try to optimize the performance of the three learning methods themselves, because this was not the goal of the experiments.…”

Section: Methodsmentioning

confidence: 99%

Learning from Ambiguously Labeled Examples

Hüllermeier

Beringer

2005

Lecture Notes in Computer Science

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Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting (nearest neighbor classification, decision tree learning, and rule induction) and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples.Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice.

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“…To improve classification accuracy, we apply feature weighting, in which the features are scaled so that their numerical range better matches their relevance [17,28]. Feature selection is implicitly included by allowing the weight of a feature to be zero.…”

Section: Genetic Optimization For Feature Weightingmentioning

confidence: 99%

Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

Lee¹,

Nelson²

2008

Artificial Intelligence in Medicine

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Objective: The purpose of this study was to develop a pattern classification algorithm for use in predicting the location of new contrast-enhancement in brain tumor patients using data obtained via multivariate magnetic resonance imaging from a prior scan. We also explore the use of feature selection or weighting in improving the accuracy of the pattern classifier. Methods and materials:Contrast-enhanced MR images, perfusion images, diffusion images, and proton spectroscopic imaging data were obtained from 26 patients with glioblastoma multiforme brain tumors, divided into a design set and an unseen test set for verification of results. A k-NN algorithm was implemented to classify unknown data based on a set of training data with ground truth derived from post-treatment contrast enhanced images; the quality of the k-NN results was evaluated using a leave-one-out cross-validation method. A genetic algorithm was implemented to select optimal features and feature weights for the k-NN algorithm. The binary representation of the weights was varied from 1 to 4 bits. Each individual parameter was thresholded as a simple classification technique, and the results compared with the k-NN. Results:The feature selection k-NN was able to achieve a sensitivity of 0.78 ± 0.18 and specificity of 0.79 ± 0.06 on the holdout test data using only 7 of the 38 original features. Similar results were obtained with non-binary weights, but using a larger number of features. Overfitting was also observed in the higher bit representations. The best single-variable classifier, based on a choline-to-NAA abnormality index computed from spectroscopic data, achieved a sensitivity of 0.79 ± 0.20 and specificity of 0.71 ± 0.11. The k-NN results had lower variation across patients than the single-variable classifiers. Conclusions:We have demonstrated that the an optimized k-NN rule could be used for quantitative analysis of multivariate images, and be applied to a specific clinical research question. Selecting features was found to be useful in improving the accuracy of feature weighting algorithms and improving the comprehensibility of the results. We believe that in addition to

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A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms

Cited by 241 publications

References 37 publications

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

Learning from Ambiguously Labeled Examples

Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

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