FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1738409213> ?p ?o ?g. }

• W1738409213 abstract "Machine learning methods are used to build models for classification and regression tasks, among others. Models are built on the basis of information contained in a set of samples, with few or no information about the underlying process. The more information there is in the set of samples, the better the model should be. However, this natural assumption does not always hold, since most machine learning paradigms suffer from the 'curse of dimensionality'. The curse of dimensionality means that strange phenomena appear when data are represented in a high-dimensional space. These phenomena are most often counter-intuitive: the conventional geometrical interpretation of data analysis in 2- or 3-dimensional spaces cannot be extended to much higher dimensions. Among the problems related to the curse of dimensionality, the feature redundancy and concentration of the norm are probably those that have the largest impact on data analysis tools. Feature redundancy means that models will lose the identifiability property (for example they will oscillate between equivalent solutions), will be difficult to interpret, etc.; although it is an advantage on the point of view of information content in the data, the redundancy makes the learning of the model more difficult. The concentration of the norm is a more specific unfortunate property of high-dimensional vectors: when the dimension of the space increases, norms and distances will concentrate, making the discrimination between data more difficult. Most data analysis tools are not robust to these phenomena. Their performance collapse when the dimension of the data space increases, in particular when the number of data available for learning is limited. This tutorial will start by a presentation of phenomena related to the curse of dimensionality. Then, feature selection will be discussed, as a possible remedy to this curse. Feature selection consists in selecting some of the variables/features among those available in the dataset, according to a relevance criterion. The goal is twofold: to avoid redundancy between features, and to discard irrelevant ones. State-of-the-art feature selection methods based on information theory criteria will be presented, together with the respective advantages of filter, wrapper and embedded methods. The tutorial will conclude by opening new research questions about feature selection with informatics theoretic criteria." @default.
• W1738409213 created "2016-06-24" @default.
• W1738409213 creator A5009959388 @default.
• W1738409213 date "2012-01-01" @default.
• W1738409213 modified "2023-09-26" @default.
• W1738409213 title "Information theoretic feature selection for high-dimensional data analysis" @default.
• W1738409213 hasPublicationYear "2012" @default.
• W1738409213 type Work @default.
• W1738409213 sameAs 1738409213 @default.
• W1738409213 citedByCount "0" @default.
• W1738409213 crossrefType "journal-article" @default.
• W1738409213 hasAuthorship W1738409213A5009959388 @default.
• W1738409213 hasConcept C111030470 @default.
• W1738409213 hasConcept C111472728 @default.
• W1738409213 hasConcept C111919701 @default.
• W1738409213 hasConcept C119857082 @default.
• W1738409213 hasConcept C122770356 @default.
• W1738409213 hasConcept C124101348 @default.
• W1738409213 hasConcept C138885662 @default.
• W1738409213 hasConcept C148483581 @default.
• W1738409213 hasConcept C152124472 @default.
• W1738409213 hasConcept C154945302 @default.
• W1738409213 hasConcept C16811321 @default.
• W1738409213 hasConcept C17744445 @default.
• W1738409213 hasConcept C189950617 @default.
• W1738409213 hasConcept C191795146 @default.
• W1738409213 hasConcept C199539241 @default.
• W1738409213 hasConcept C21080849 @default.
• W1738409213 hasConcept C41008148 @default.
• W1738409213 hasConcept C70518039 @default.
• W1738409213 hasConcept C83665646 @default.
• W1738409213 hasConceptScore W1738409213C111030470 @default.
• W1738409213 hasConceptScore W1738409213C111472728 @default.
• W1738409213 hasConceptScore W1738409213C111919701 @default.
• W1738409213 hasConceptScore W1738409213C119857082 @default.
• W1738409213 hasConceptScore W1738409213C122770356 @default.
• W1738409213 hasConceptScore W1738409213C124101348 @default.
• W1738409213 hasConceptScore W1738409213C138885662 @default.
• W1738409213 hasConceptScore W1738409213C148483581 @default.
• W1738409213 hasConceptScore W1738409213C152124472 @default.
• W1738409213 hasConceptScore W1738409213C154945302 @default.
• W1738409213 hasConceptScore W1738409213C16811321 @default.
• W1738409213 hasConceptScore W1738409213C17744445 @default.
• W1738409213 hasConceptScore W1738409213C189950617 @default.
• W1738409213 hasConceptScore W1738409213C191795146 @default.
• W1738409213 hasConceptScore W1738409213C199539241 @default.
• W1738409213 hasConceptScore W1738409213C21080849 @default.
• W1738409213 hasConceptScore W1738409213C41008148 @default.
• W1738409213 hasConceptScore W1738409213C70518039 @default.
• W1738409213 hasConceptScore W1738409213C83665646 @default.
• W1738409213 hasLocation W17384092131 @default.
• W1738409213 hasOpenAccess W1738409213 @default.
• W1738409213 hasPrimaryLocation W17384092131 @default.
• W1738409213 hasRelatedWork W1506948633 @default.
• W1738409213 hasRelatedWork W1557869917 @default.
• W1738409213 hasRelatedWork W155899715 @default.
• W1738409213 hasRelatedWork W1808765063 @default.
• W1738409213 hasRelatedWork W1936070845 @default.
• W1738409213 hasRelatedWork W1982398155 @default.
• W1738409213 hasRelatedWork W2060752380 @default.
• W1738409213 hasRelatedWork W2061385089 @default.
• W1738409213 hasRelatedWork W2077346862 @default.
• W1738409213 hasRelatedWork W2120353692 @default.
• W1738409213 hasRelatedWork W2149943934 @default.
• W1738409213 hasRelatedWork W2574219039 @default.
• W1738409213 hasRelatedWork W2804705540 @default.
• W1738409213 hasRelatedWork W2806381151 @default.
• W1738409213 hasRelatedWork W2905148207 @default.
• W1738409213 hasRelatedWork W2945258978 @default.
• W1738409213 hasRelatedWork W2949890540 @default.
• W1738409213 hasRelatedWork W3013302231 @default.
• W1738409213 hasRelatedWork W3167352803 @default.
• W1738409213 hasRelatedWork W2182011477 @default.
• W1738409213 isParatext "false" @default.
• W1738409213 isRetracted "false" @default.
• W1738409213 magId "1738409213" @default.
• W1738409213 workType "article" @default.