Open AccessHigh‐accuracy document classification with a new algorithm
Author(s) -
Temel T.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0790
Subject(s) - python (programming language) , computer science , classifier (uml) , artificial intelligence , algorithm , statistical classification , weight , machine learning , pattern recognition (psychology) , data mining , mathematics , lie algebra , pure mathematics , operating system
A new algorithm based on learning vector quantisation classifier is presented based on a modified proximity‐measure, which enforces a predetermined correct classification level in training while using sliding‐mode approach for stable variation in weight updates towards convergence. The proposed algorithm and some well‐known counterparts are implemented by using Python libraries and compared in a task of text classification for document categorisation. Results reveal that the new classifier is a successful contender to those algorithms in terms of testing and training performances.