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Erschienen in:
Three-Dimensional Model of Signal Processing in the Presynaptic Bouton of the Neuron Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Probabilistic Feature Selection in Machine Learning

verfasst von : Indrajit Ghosh

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

In machine learning, Case Based Reasoning is a prominent technique for harvesting knowledge from past experiences. The past experiences are represented in the form of a repository of cases having a set of features. But each feature may not have the equal relevancy in describing a case. Measuring the relevancy of each feature is always a prime issue. A subset of relevant features describes a case with adequate accuracy. An appropriate subset of relevant features should be selected for improving the performance of the system and to reduce dimensionality. In case based domain, feature selection is a process of selecting an appropriate subset of relevant features. There are various real domains which are inherently case based and features are expressed in terms of linguistic variables. To assign a numerical weight to each linguistic feature, a lot of feature subset selection algorithms have been proposed. But the weighting values are usually determined using subjective judgement or a trial and error basis.
This work presents an alternative concept in this direction. It can be efficiently applied to select the relevant linguistic features by measuring the probability in term of numerical values. It can also rule out irrelevant and noisy features. Applications of this approach in various real world domain show an excellent performance.

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Vorheriges Kapitel Application of the Bag-of-Words Algorithm in Classification the Quality of Sales Leads
Nächstes Kapitel Boost Multi-class sLDA Model for Text Classification
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Metadaten
Titel
Probabilistic Feature Selection in Machine Learning
verfasst von
Indrajit Ghosh
Copyright-Jahr
2018
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-91253-0_58