Faculty Publications
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Item An efficient dynamic switching algorithm for mining colossal closed itemsets from high dimensional datasets(Elsevier B.V., 2019) Vanahalli, M.K.; Patil, N.The abundant data across a variety of domains including bioinformatics has led to the formation of dataset with high dimensionality. The conventional algorithms expend most of their time in mining a large number of small and mid-sized itemsets which does not enclose complete and valuable information for decision making. The recent research is focused on Frequent Colossal Closed Itemsets (FCCI), which plays a significant role in decision making for many applications, especially in the field of bioinformatics. The state-of-the-art algorithms in mining FCCI from datasets consisting of a large number of rows and a large number of features are computationally expensive, as they are either pure row or feature enumeration based algorithms. Moreover, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. The proposed work emphasizes an Effective Improvised Preprocessing (EIP) technique to prune the complete set of irrelevant features and irrelevant rows, and a novel efficient Dynamic Switching Frequent Colossal Closed Itemset Mining (DSFCCIM) algorithm. The proposed DSFCCIM algorithm efficiently switches between row and feature enumeration methods based on data characteristics during the mining process. Further, the DSFCCIM algorithm is integrated with a novel Rowset Cardinality Table, Itemset Support Table, two efficient methods to check the closeness of rowset and itemset, and two efficient pruning strategies to cut down the search space. The proposed DSFCCIM algorithm is the first dynamic switching algorithm to mine FCCI from datasets consisting of a large number of rows and a large number of features. The performance study shows the improved effectiveness of the proposed EIP technique over the existing preprocessing techniques and the improved efficiency of the proposed DSFCCIM algorithm over the existing algorithms. © 2019 Elsevier B.V.Item Enhanced protein structural class prediction using effective feature modeling and ensemble of classifiers(Institute of Electrical and Electronics Engineers Inc., 2021) Bankapur, S.; Patil, N.Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Identification of PSSC using biological methods is time-consuming and cost-intensive. Several computational models have been developed to predict the structural class; however, they lack in generalization of the model. Hence, predicting PSSC based on protein sequences is still proving to be an uphill task. In this article, we proposed an effective, novel and generalized prediction model consisting of a feature modeling and an ensemble of classifiers. The proposed feature modeling extracts discriminating information (features) by leveraging three techniques: (i) Embedding – features are extracted on the basis of spatial residue arrangements of the sequences using word embedding approaches; (ii) SkipXGram Bi-gram – various sets of skipped bi-gram features are extracted from the sequences; and (iii) General Statistical (GS) based features are extracted which covers the global information of structural sequences. The combined effective sets of features are trained and classified using an ensemble of three classifiers: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). The proposed model when assessed on five benchmark datasets (high and low sequence similarity), viz. z277, z498, 25PDB, 1189, and FC699, reported an overall accuracy of 93.55, 97.58, 81.82, 81.11, and 93.93 percent respectively. The proposed model is further validated on a large-scale updated low similarity (?25%) dataset, where it achieved an overall accuracy of 81.11 percent. The proposed generalized model is robust and consistently outperformed several state-of-the-art models on all the five benchmark datasets. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.