Classification of confidential documents by using adaptive neurofuzzy inference systems
Erdem Alparslan, Adem Karahoca, Hayretdin Bahşi · Procedia Computer Science · 2011
Detecting the security level of a confidential document is a vital task for organizations to protect the confidential information encapsulated in. Diverse classification rules and techniques are being applied by human experts. Increasing number of confidential information in organizations are making difficult to classify all the documents carefully with human effort. A hybrid approach involving support vector classifier and adaptive neuro-fuzzy classifier is proposed in this study. Also states preprocessing tasks required for document classification with natural language processing. To represent term-document relations a recommended metric TF-IDF was chosen to construct a weight matrix. Agglutinative nature of Turkish documents is handled by Turkish stemming algorithms. At the end of the article some experimental results and success metrics are projected with accuracy rates.