Constructive Induction for Classifying Time Series
We present a method of constructive induction aimed at learning tasks involving multivariate time series data. Using metafeatures, the scope of attribute-value learning is expanded to domains that contain instances that have some kind of recurring substructure, such as strokes in handwriting recogni...
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| creator | Kadous, Mohammed Waleed Sammut, Claude |
| description | We present a method of constructive induction aimed at learning tasks involving multivariate time series data. Using metafeatures, the scope of attribute-value learning is expanded to domains that contain instances that have some kind of recurring substructure, such as strokes in handwriting recognition, or local maxima in time series data. These substructures are used to construct attributes. Metafeatures are applied to two real-world domains: sign language recognition and ECG classification. Using a very generic set of metafeatures we are able to generate classifiers that are either comprehensible or accurate, producing results that are comparable to hand-crafted preprocessing and comparable to human experts. |
| doi_str_mv | 10.1007/978-3-540-30115-8_20 |
| format | Book Chapter |
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Using metafeatures, the scope of attribute-value learning is expanded to domains that contain instances that have some kind of recurring substructure, such as strokes in handwriting recognition, or local maxima in time series data. These substructures are used to construct attributes. Metafeatures are applied to two real-world domains: sign language recognition and ECG classification. Using a very generic set of metafeatures we are able to generate classifiers that are either comprehensible or accurate, producing results that are comparable to hand-crafted preprocessing and comparable to human experts.</description><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>constructive induction</subject><subject>Exact sciences and technology</subject><subject>Information systems. Data bases</subject><subject>machine learning</subject><subject>Memory organisation. 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Data processing</topic><topic>Software</topic><topic>time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kadous, Mohammed Waleed</creatorcontrib><creatorcontrib>Sammut, Claude</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kadous, Mohammed Waleed</au><au>Sammut, Claude</au><au>Boulicaut, Jean-François</au><au>Giannotti, Fosca</au><au>Esposito, Floriana</au><au>Pedreschi, Dino</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Constructive Induction for Classifying Time Series</atitle><btitle>Lecture notes in computer science</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2004</date><risdate>2004</risdate><spage>192</spage><epage>204</epage><pages>192-204</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540231059</isbn><isbn>3540231056</isbn><eisbn>3540301151</eisbn><eisbn>9783540301158</eisbn><abstract>We present a method of constructive induction aimed at learning tasks involving multivariate time series data. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2004, p.192-204 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16144316 |
| source | Springer Books |
| subjects | Applied sciences Computer science control theory systems constructive induction Exact sciences and technology Information systems. Data bases machine learning Memory organisation. Data processing Software time series |
| title | Constructive Induction for Classifying Time Series |
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