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This paper provides a survey of existing pruning techniques that optimize the architecture of neural networks and discusses their advantages and limitations. Also the paper evaluates the effectiveness of various pruning techniques by comparing the performance of some traditional and recent pruning algorithms based on sensitivity analysis, mutual information and significance on four real datasets namely Iris, Wisconsin breast cancer, Hepatitis Domain and Pima Indian Diabetes.<\/jats:p>","DOI":"10.2478\/s13537-013-0109-x","type":"journal-article","created":{"date-parts":[[2013,9,24]],"date-time":"2013-09-24T14:07:24Z","timestamp":1380031644000},"source":"Crossref","is-referenced-by-count":66,"title":["Pruning algorithms of neural networks \u2014 a comparative study"],"prefix":"10.2478","volume":"3","author":[{"given":"M.","family":"Augasta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Kathirvalavakumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","reference":[{"issue":"4","key":"109_CR1","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","volume":"18","author":"P M Atkinson","year":"1997","unstructured":"P. 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