Investigation of BPNN &amp;amp; RBFN in text classification by Active search

Motwani, Mahak; Tiwari, Aruna; Sharma, Sanjeev

doi:10.1109/icecct.2015.7226042

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…There are hardiness to noisy data [37,38] and has an excellent achievement to handle small data set [39,40]. A study by [41] has shown with the F1 measure that RBFN has better performance and more consistent than BPNN to perform text classification on small newsgroup dataset.…”

Section: Classification Stagementioning

confidence: 99%

New instances classification framework on Quran ontology applied to question answering system

2019

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Instances classification with the small dataset for Quran ontology is the current research problem which appears in Quran ontology development. The existing classification approach used machine learning: Backpropagation Neural Network. However, this method has a drawback; if the training set amount is small, then the classifier accuracy could decline. Unfortunately, Holy Quran has a small corpus. Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. This algorithm is chosen since it robustness to noisy data and has an excellent achievement to handle small dataset. Furthermore, document processing module on question answering system is used to access instances classification result in Quran ontology.

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