Faculty Publications
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Item A novel technique of feature selection with relieff and CFS for protein sequence classification(Springer Verlag service@springer.de, 2019) Kaur, K.; Patil, N.Bioinformatics has gained wide importance in research area for the last few decades. The main aim is to store the biological data and analyze it for better understanding. To predict the functions of newly added protein sequences, the classification of existing protein sequence is of great use. The rate at which protein sequence data is getting accumulated is increasing exponentially. So, it emerges as a very challenging task for the researcher, to deal with large number of features obtained by the use of various encoding techniques. Here, a two-stage algorithm is proposed for feature selection that combines ReliefF and CFS technique that takes extracted features as input and provides us with the discriminative set of features. The n-gram sequence encoding technique has been used to extract the feature vector from the protein sequences. In the first stage, ReliefF approach is used to rank the features and obtain candidate feature set. In the second stage, CFS is applied on this candidate feature set to obtain features that have high correlation with the class but less correlation with other features. The classification methods like Naive-Bayes, decision tree, and k-nearest neighbor can be used to analyze the performance of proposed approach. It is observed that this approach has increased accuracy of classification methods in comparison to existing methods. © Springer Nature Singapore Pte Ltd. 2019Item Genome Data Analysis Using MapReduce Paradigm(Institute of Electrical and Electronics Engineers Inc., 2015) Pahadia, M.; Srivastava, A.; Srivastava, D.; Patil, N.Counting the number of occurences of a substringin a string is a problem in many applications. This paper suggests a fast and efficient solution for the field of bioinformatics. Ak-mer is a k-length sub string of a biological sequence. K-mercounting is defined as counting the number of occurences of all the possible k-mers in a biological sequence. K-mer counting has uses in applications ranging from error correction of sequencing reads, genome assembly, disease prediction and feature extraction. The current k-mer counting tools are both time and space costly. We provide a solution which uses MapReduce and Hadoop to reduce the time complexity. After applying the algorithms on real genome datasets, we concluded that the algorithm using Hadoopand MapReduce Paradigm runs more efficiently and reduces the time complexity significantly. © 2015 IEEE.Item Distributed mining of significant frequent colossal closed itemsets from long biological dataset(Springer Verlag service@springer.de, 2020) Vanahalli, M.K.; Patil, N.Mining colossal itemsets have gained more attention in recent times. An extensive set of short and average sized itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining these little and average sized itemsets. Colossal itemsets are very significant for numerous applications including the field of bioinformatics and are influential during the decision making. The new mode of dataset known as long biological dataset was contributed by Bioinformatics. These datasets are high dimensional datasets, which are depicted by an expansive number of features (attributes) and a less number of rows (samples). Extracting huge amount of information and knowledge from high dimensional long biological dataset is a nontrivial task. The existing algorithms are computationally expensive and sequential in mining significant Frequent Colossal Closed itemsets (FCCI) from long biological dataset. Distributed computing is a good strategy to overcome the inefficiency of the existing sequential algorithm. The paper proposes a distributed computing approach for mining FCCI. The row enumerated mining search space is efficiently cut down by pruning strategy enclosed in Distributed Row Enumerated Frequent Colossal Closed Itemset Mining (DREFCCIM) algorithm. The proposed DREFCCIM algorithm is the first distributed algorithm to mine FCCI from long biological dataset. The experimental results demonstrate the efficient performance of the DREFCCIM algorithm in comparison to the current algorithms. © Springer Nature Switzerland AG 2020.Item A novel filter–wrapper hybrid greedy ensemble approach optimized using the genetic algorithm to reduce the dimensionality of high-dimensional biomedical datasets(Elsevier Ltd, 2019) Gangavarapu, T.; Patil, N.The predictive accuracy of high-dimensional biomedical datasets is often dwindled by many irrelevant and redundant molecular disease diagnosis features. Dimensionality reduction aims at finding a feature subspace that preserves the predictive accuracy while eliminating noise and curtailing the high computational cost of training. The applicability of a particular feature selection technique is heavily reliant on the ability of that technique to match the problem structure and to capture the inherent patterns in the data. In this paper, we propose a novel filter–wrapper hybrid ensemble feature selection approach based on the weighted occurrence frequency and the penalty scheme, to obtain the most discriminative and instructive feature subspace. The proposed approach engenders an optimal feature subspace by greedily combining the feature subspaces obtained from various predetermined base feature selection techniques. Furthermore, the base feature subspaces are penalized based on specific performance dependent penalty parameters. We leverage effective heuristic search strategies including the greedy parameter-wise optimization and the Genetic Algorithm (GA) to optimize the subspace ensembling process. The effectiveness, robustness, and flexibility of the proposed hybrid greedy ensemble approach in comparison with the base feature selection techniques, and prolific filter and state-of-the-art wrapper methods are justified by empirical analysis on three distinct high-dimensional biomedical datasets. Experimental validation revealed that the proposed greedy approach, when optimized using GA, outperformed the selected base feature selection techniques by 4.17%–15.14% in terms of the prediction accuracy. © 2019 Elsevier B.V.Item An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets(Elsevier Inc. usjcs@elsevier.com, 2019) Vanahalli, M.K.; Patil, N.Mining colossal itemsets from high dimensional datasets have gained focus in recent times. The conventional algorithms expend most of the time in mining small and mid-sized itemsets, which do not enclose valuable and complete information for decision making. Mining Frequent Colossal Closed Itemsets (FCCI) from a high dimensional dataset play a highly significant role in decision making for many applications, especially in the field of bioinformatics. To mine FCCI from a high dimensional dataset, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. Besides, the state-of-the-art algorithms for the same are sequential and computationally expensive. The proposed work highlights an Effective Improved Parallel Preprocessing (EIPP) technique to prune the complete set of irrelevant features and irrelevant rows from high dimensional dataset and a novel efficient Parallel Frequent Colossal Closed Itemset Mining (PFCCIM) algorithm. Further, the PFCCIM algorithm is integrated with a novel Rowset Cardinality Table (RCT), an efficient method to check the closeness of a rowset and also an efficient pruning strategy to cut down the mining search space. The proposed PFCCIM algorithm is the first parallel algorithm to mine FCCI from a high dimensional dataset. The performance study shows the improved effectiveness of the proposed EIPP technique over the existing preprocessing techniques and the improved efficiency of the proposed PFCCIM algorithm over the existing algorithms. © 2018 Elsevier Inc.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 A fast and novel approach based on grouping and weighted mRMR for feature selection and classification of protein sequence data(Inderscience Publishers, 2020) Kaur, K.; Patil, N.The analysis of protein sequences under bioinformatics has gained wide importance in research area. Newly added protein sequences can be analysed using existing proteins and converting them into feature vector form. However, it emerges as a challenging task to deal with huge number of features obtained using sequence encoding techniques. Since all the features obtained are not actually required, a three-stage feature selection approach has been proposed. In the first stage, features are ranked and most irrelevant features are removed; in the second stage, conflicting features are grouped together; and in third stage, a fast approach based on weighted Minimum Redundancy Maximum Relevance (wMRMR) has been proposed and applied on grouped features. Different classification methods are used to analyse the performance of the proposed approach. It is observed that the proposed approach has increased classification accuracy results and reduced time consumption in comparison to the state-of-the-art methods. © 2020 Inderscience Enterprises Ltd.Item Distributed load balancing frequent colossal closed itemset mining algorithm for high dimensional dataset(Academic Press Inc. apjcs@harcourt.com, 2020) Vanahalli, M.K.; Patil, N.The focus of extracting colossal closed itemsets from high dimensional biological datasets has been great in recent times. A massive set of short and average sized mined itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining a massive set of short and average sized itemsets. The greater interest of research in the field of bioinformatics and the abundant data across the variety of domains paved the way for the generation of the high dimensional dataset. These datasets are depicted by an extensive number of features and a smaller number of rows. Colossal closed itemsets are very significant for numerous applications including the field of bioinformatics and are influential during the decision making. Extracting a huge amount of information and knowledge from the high dimensional dataset is a nontrivial task. The existing colossal closed itemsets mining algorithms for the high dimensional dataset are sequential and computationally expensive. Distributed and parallel computing is a good strategy to overcome the inefficiency of the existing sequential algorithm. Balanced Distributed Parallel Frequent Colossal Closed Itemset Mining (BDPFCCIM) algorithm is designed for high dimensional datasets. An efficient closeness checking method to check the closeness of the rowset and an efficient pruning strategy to snip the row enumeration mining search space is enclosed with the proposed BDPFCCIM algorithm. The proposed BDPFCCIM algorithm is the first distributed load balancing algorithm to mine frequent colossal closed itemsets from high dimensional biological datasets. The experimental results demonstrate the efficient performance of the proposed BDPFCCIM algorithm in comparison with the state-of-the-art algorithms. © 2020 Elsevier Inc.Item An efficient colossal closed itemset mining algorithm for a dataset with high dimensionality(King Saud bin Abdulaziz University, 2022) Vanahalli, M.K.; Patil, N.The greater interest of research in the field of bioinformatics and the ample amount of available data across the different domains paved the way for the generation of the dataset with high dimensionality. The number of features in the dataset with high dimensionality are very high and number of rows are less. The significance of the Frequent Colossal Closed Itemsets (FCCI) is high for diverse applications and also for the field of bioinformatics. FCCI are very prominent in the process of the decision making. Amount of information extraction from the dataset with high dimensionality is huge and this extraction is a non-trivial task. The pruning of all the inadmissible features and rows is not performed by the state-of-the-art algorithms. The proposed work articulates the pruning of all the inadmissible features and rows, an efficient pruning strategy to snip the row enumeration mining search space and closure method for checking the closeness of the rowset. An efficient row enumeration algorithm enclosing the rowset closure checking method and pruning strategy is designed to efficiently mine the complete set of FCCI. The experimental results demonstrate the effectiveness of pruning all the inadmissible features and rows. © 2020 The Authors