Matches in SemOpenAlex for { <https://semopenalex.org/work/W2234687539> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W2234687539 abstract "In order to get effective information timely and accurately in masses of text, text classification techniques get extensive attention from many aspects. A lot of algorithms were proposed for text classification which made it easy to classify texts, such as Naive Bayes, Rocchio, Decision Tree, Artificial Neural Networks, VSM, kNN and so on. In this paper, we mainly discussed the latest improved algorithm of kNN including Rocchio-kNN, TW-kNN, RS-kNN and kNN based on K-Medoids. Each of the representative algorithms is discussed in detail. These algorithms based on kNN have reduced the computational complexity as well as increased the execution efficiency compared with the traditional kNN algorithm. Keywords-text clasificaton; rocchio-Knn; TW-kNN; RS-kNN; kNN based on K-Medoids I INTRODUCTION Text classification is a kind of procedure related with NLP (Natural Language Processing). It finds relational mode (classifier) between text's attributes (feature) and text's category according to a labeled training text corpus, then utilizes the classifier to classify new text corpus. Text classification can be divided into two parts: training and classifying. The purpose of training is to structure classifier which can be used to classify new texts by the connection between training text and category. Classifying means to make the unknown new text assigned with the known category label. The procedure of text classification is showed in Figure.1. (Process I is the procedure of training, Process II is the procedure of classifying.)" @default.
- W2234687539 created "2016-06-24" @default.
- W2234687539 creator A5042221444 @default.
- W2234687539 creator A5054498963 @default.
- W2234687539 date "2015-01-01" @default.
- W2234687539 modified "2023-09-25" @default.
- W2234687539 title "A Review of a Text Classification Technique: K-Nearest Neighbor" @default.
- W2234687539 cites W1563014322 @default.
- W2234687539 cites W1973965874 @default.
- W2234687539 cites W1979346010 @default.
- W2234687539 cites W1997362234 @default.
- W2234687539 cites W2043772506 @default.
- W2234687539 cites W2058089741 @default.
- W2234687539 cites W2122111042 @default.
- W2234687539 cites W2149772057 @default.
- W2234687539 cites W2150747245 @default.
- W2234687539 cites W2164547069 @default.
- W2234687539 cites W2168628284 @default.
- W2234687539 cites W2327021392 @default.
- W2234687539 cites W2978533537 @default.
- W2234687539 cites W84420897 @default.
- W2234687539 doi "https://doi.org/10.2991/cisia-15.2015.123" @default.
- W2234687539 hasPublicationYear "2015" @default.
- W2234687539 type Work @default.
- W2234687539 sameAs 2234687539 @default.
- W2234687539 citedByCount "1" @default.
- W2234687539 countsByYear W22346875392016 @default.
- W2234687539 crossrefType "proceedings-article" @default.
- W2234687539 hasAuthorship W2234687539A5042221444 @default.
- W2234687539 hasAuthorship W2234687539A5054498963 @default.
- W2234687539 hasBestOaLocation W22346875391 @default.
- W2234687539 hasConcept C110083411 @default.
- W2234687539 hasConcept C113238511 @default.
- W2234687539 hasConcept C119857082 @default.
- W2234687539 hasConcept C12267149 @default.
- W2234687539 hasConcept C124101348 @default.
- W2234687539 hasConcept C153180895 @default.
- W2234687539 hasConcept C154945302 @default.
- W2234687539 hasConcept C204321447 @default.
- W2234687539 hasConcept C41008148 @default.
- W2234687539 hasConcept C52001869 @default.
- W2234687539 hasConcept C71472368 @default.
- W2234687539 hasConcept C84525736 @default.
- W2234687539 hasConcept C95623464 @default.
- W2234687539 hasConceptScore W2234687539C110083411 @default.
- W2234687539 hasConceptScore W2234687539C113238511 @default.
- W2234687539 hasConceptScore W2234687539C119857082 @default.
- W2234687539 hasConceptScore W2234687539C12267149 @default.
- W2234687539 hasConceptScore W2234687539C124101348 @default.
- W2234687539 hasConceptScore W2234687539C153180895 @default.
- W2234687539 hasConceptScore W2234687539C154945302 @default.
- W2234687539 hasConceptScore W2234687539C204321447 @default.
- W2234687539 hasConceptScore W2234687539C41008148 @default.
- W2234687539 hasConceptScore W2234687539C52001869 @default.
- W2234687539 hasConceptScore W2234687539C71472368 @default.
- W2234687539 hasConceptScore W2234687539C84525736 @default.
- W2234687539 hasConceptScore W2234687539C95623464 @default.
- W2234687539 hasLocation W22346875391 @default.
- W2234687539 hasOpenAccess W2234687539 @default.
- W2234687539 hasPrimaryLocation W22346875391 @default.
- W2234687539 hasRelatedWork W1497227544 @default.
- W2234687539 hasRelatedWork W1999613826 @default.
- W2234687539 hasRelatedWork W2000635765 @default.
- W2234687539 hasRelatedWork W2004633764 @default.
- W2234687539 hasRelatedWork W2092022777 @default.
- W2234687539 hasRelatedWork W2098243239 @default.
- W2234687539 hasRelatedWork W2102798330 @default.
- W2234687539 hasRelatedWork W2121082043 @default.
- W2234687539 hasRelatedWork W2139805022 @default.
- W2234687539 hasRelatedWork W2349230575 @default.
- W2234687539 hasRelatedWork W2353319460 @default.
- W2234687539 hasRelatedWork W2359227732 @default.
- W2234687539 hasRelatedWork W2359932463 @default.
- W2234687539 hasRelatedWork W2364529029 @default.
- W2234687539 hasRelatedWork W2372764011 @default.
- W2234687539 hasRelatedWork W2585258431 @default.
- W2234687539 hasRelatedWork W2602999615 @default.
- W2234687539 hasRelatedWork W2612554835 @default.
- W2234687539 hasRelatedWork W2900235674 @default.
- W2234687539 hasRelatedWork W2922394742 @default.
- W2234687539 isParatext "false" @default.
- W2234687539 isRetracted "false" @default.
- W2234687539 magId "2234687539" @default.
- W2234687539 workType "article" @default.