Naïve bayes classification of DRDO tender documents

We propose a technique of automatic classification of DRDO tender documents into predefined technological categories. The dataset comprised of 698 tender files obtained from DRDO website. The dataset was processed and fed into Weka toolkit. Experiments were conducted using Naïve Bayes classifier with 10 folds cross validation. The documents were classified with 75.21 percent accuracy by technology area and 68.62 percent by lab´s name when the dataset included lab names somewhere in the document. An accuracy result of 74.36 percent and 68.34 percent was attained for the technology area and name of the lab respectively when all occurrences of lab names were removed from the dataset. This experiment is a step to train auto-classification of the tender documents available on Internet for Selective Dissemination of Information to the Concerned Vendors. Also, this is an attempt to gauge the area of work, undergoing tasks and the exact work being carried by an organisation based on the tenders published by it.