Scientific　publications　has　been　increasing　enormously,　with　this　increase　classification　of　scientific　publications　is　becoming　challenging　task.　The　core　objective　of　this　research　is　to　analyze　the　performance　of　classification　algorithms　using　Scopus　dataset.　In　text　classification,　classification　and　feature　extraction　from　the　document　using　extracted　features　are　the　major　issues　for　decreasing　the　performances　in　different　algorithms.　In　this　paper,　performances　of　classification　algorithms　such　as　Naive　Bayes　(NB)　and　K-Nearest　Neighbor　(K-NN)　shown　better　improvement　using　Bayesian　boost　and　bagging.　The　performance　results　were　analyzed　through　selected　classification　algorithms　over　10K　documents　from　Scopus　examined　using　F-measure　and　produced　comparison　matrices　to　estimate　accuracy,　precision　and　recall　using　NB　and　KNN　classifier.　Further,　data　preprocessing　and　cleaning　steps　are　induced　on　the　selected　dataset　and　class　imbalance　issues　are　analyzed　to　increase　the　performance　of　text　classification　algorithms.　Experimental　results　showed　performances　over　7%　using　K-NN　and　revealed　better　as　compared　to　NB.