Browsing by Author "Anoop Kumar Tiwari"

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A fitting model based intuitionistic fuzzy rough feature selection
(Elsevier Ltd, 2020) Pankhuri Jain; Anoop Kumar Tiwari; Tanmoy Som
Feature subset selection is an essential machine learning approach aimed at the process of dimensionality reduction of the input space. By removing irrelevant and/or redundant variables, not only it enhances model performance, but also facilitates its improved interpretability. The fuzzy set and the rough set are two different but complementary theories that apply the fuzzy rough dependency as a criterion for performing feature subset selection. However, this concept can only maintain a maximal dependency function. It cannot preferably illustrate the differences in object classification and does not fit a particular dataset well. This problem was handled by using a fitting model for feature selection with fuzzy rough sets. However, intuitionistic fuzzy set theory can deal with uncertainty in a much better way when compared to fuzzy set theory as it considers positive, negative and hesitancy degree of an object simultaneously to belong to a particular set. Therefore, in the current study, a novel intuitionistic fuzzy rough set model is proposed for handling above mentioned problems. This model fits the data well and prevents misclassification. Firstly, intuitionistic fuzzy decision of a sample is introduced using neighborhood concept. Then, intuitionistic fuzzy lower and upper approximations are constructed using intuitionistic fuzzy decision and parameterized intuitionistic fuzzy granule. Furthermore, a new dependency function is established. Moreover, a greedy forward algorithm is given using the proposed concept to calculate reduct set. Finally, this algorithm is applied to the benchmark datasets and a comparative study with the existing algorithm is presented. From the experimental results, it can be observed that the proposed model provides more accurate reduct set than existing model. © 2019
An intuitionistic fuzzy bireduct model and its application to cancer treatment
(Elsevier Ltd, 2022) Pankhuri Jain; Anoop Kumar Tiwari; Tanmoy Som
Due to technological advancement, data size has seen a significant increase both in terms of features and instances. An efficient way to handle large sized datasets is to apply data reduction technique to ease the computational intractability of learning algorithms. Intuitionistic fuzzy rough and fuzzy rough sets based approaches provide a handful of solution, but the applicability is mostly restricted to reduction in terms of feature size. However, reduction in terms of instance size has not been handled by any of the existing intuionistic fuzzy set approaches. The present paper introduces the notion of bireducts in intuitionistic fuzzy framework that can be used for simultaneous reduction of instances and features. A robust lower approximation formulation is employed along with laying the foundation for the variants of instance selection technique. The proposed model is therefore robust to noise and can very effectively handle uncertainities due to judgement as well as identification. Further, an efficient instance selection technique in bireduct formulation enhances the performance. The experimental evaluation on benchmark datasets demonstrates the applicability and robustness of the proposed bireducts. It significantly reduces data size both in terms of instances and features whilst maintaining high performances. Further, the model is applied in the challenging domain of cancer treatment by enhancing the prediction performance of anti-angiogenic peptides. A comparative analysis demonstrates the superiority of the proposed methodology. © 2022
An intuitionistic fuzzy-rough set model and its application to feature selection
(IOS Press, 2019) Anoop Kumar Tiwari; Shivam Shreevastava; Karthikeyan Subbiah; T. Som
Due to the development of modern internet-based technology, the electronically stored information is growing exponentially with time. It is highly challenging to select relevant and non-redundant features of the real-valued high dimensional datasets. Feature selection, a preprocessing technique, refers to the process of reducing the dimension of the input data in order to extract the most meaningful features for processing and analysis. One of the numerous useful applications of rough set theory is the attribute or feature selection, but it has certain limitations as it cannot be applied on real-valued data sets directly because rough set based feature selection can handle discrete data only. In order to deal with real-valued data sets, discretization method is applied to convert dataset from real-valued to discrete, which usually leads to information loss. Fuzzy rough set theory is profitably applied to address this problem and retain the semantics of real-valued datasets. However, intuitionistic fuzzy set can deal with uncertainty in a much better way when compared to fuzzy set theory as it considers membership, non-membership and hesitancy degree of an object simultaneously. In this paper, an intuitionistic fuzzy rough set model is established by combining intuitionistic fuzzy set and rough set. Furthermore, we propose a novel approach of feature selection derived from this model. Moreover, we develop an algorithm based on our proposed concept. Finally, our approach is applied to some benchmark data sets and compared with the existing fuzzy rough set based technique. The performed experiments show the superiority of our approach. © 2019 - IOS Press and the authors.
Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications
(Nature Research, 2024) Rajat Saini; Anoop Kumar Tiwari; Abhigyan Nath; Phool Singh; S.P. Maurya; Mohd Asif Shah
The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides. © The Author(s) 2024.
Deciphering oral cancer subtypes: Integrating differential gene expression and pathway analysis followed by non-negative matrix factorization transcription analysis
(Elsevier Ltd, 2025) Anoop Kumar Tiwari; Devansh Jain; Jayesh Kumar Tiwari; Shyam Kishore; Akhilesh Kumar Singh; Sushant Kumar Shrivastava; Arun Khattri
Oral cancer is a major public health concern around the globe, and its classification relies on factors such as habitual status and tumor stages. However, a significant gap exists in understanding oral cancer patients' molecular and genomic characteristics. This study aims to bridge this gap by analyzing International Cancer Genome Consortium (ICGC's) oral cancer data, which identified 2270 differentially expressed genes related to oral cancer. We employed pathway enrichment analysis, highlighting key pathways including hypoxia, VEGF, PI3K, and TGF-β, and STAT2, E2F4, and SP1 transcription factors enriched in tumor samples compared to normal samples. Moreover, we utilized a non-negative matrix factorization (NMF) technique for unsupervised subtype discovery and identified three distinct tumor subgroups. Each subgroup exhibited unique molecular profiles, with pathways related to TNF-α, NF-κB, and hypoxia enriched across all groups. Notably, transcription factor analysis revealed crucial differences: subgroup A was enriched in EGR1, TP53, and HIF1A; subgroup B showed high levels of CDX2 and HNF4A; while subgroup C was characterized by enrichment in ATF4 and E2F4. These findings suggest the feasibility of classifying oral squamous cell carcinoma (OSCC) patients based on gene expression profiles, laying a foundational framework for future research aimed at personalized treatment strategies. © 2025 The Authors
Effect of varying degree of resampling on prediction accuracy for observed peptide count in protein mass spectrometry data
(IEEE Computer Society, 2016) Anoop Kumar Tiwari; Abhigyan Nath; Karthikeyan Subbiah; Kaushal Kumar Shukla
Class imbalance affects the learning of classifiers and it is almost ubiquitous in biological data sets. Resampling methods are one of the common methods for balancing imbalanced data sets. SMOTE (Synthetic Minority Oversampling Techniques) is one of the intelligent methods of oversampling. This study examines the performance of learning of machine learning algorithms at different balancing ratios of positive and negative samples in the training set, consisting of the observed peptides and absent peptides in MS experiment. Using SMOTE at different rates we achieved the best result with optimal balancing on boosted random forest that resulted in sensitivity of 92.1%, specificity value of 94.7%, and overall accuracy of 93.4%, MCC of 0.869 and AUC of 0.982 that are better than previously reported results. From the results of current experiments, it can be inferred that suitably modifying the class distribution, the performance of machine learning algorithms on the classification tasks can be enhanced. © 2015 IEEE.
Enhanced Prediction for Observed Peptide Count in Protein Mass Spectrometry Data by Optimally Balancing the Training Dataset
(World Scientific Publishing Co. Pte Ltd, 2017) Anoop Kumar Tiwari; Abhigyan Nath; Karthikeyan Subbiah; Kaushal Kumar Shukla
Imbalanced dataset affects the learning of classifiers. This imbalance problem is almost ubiquitous in biological datasets. Resampling is one of the common methods to deal with the imbalanced dataset problem. In this study, we explore the learning performance by varying the balancing ratios of training datasets, consisting of the observed peptides and absent peptides in the Mass Spectrometry experiment on the different machine learning algorithms. It has been observed that the ideal balancing ratio has yielded better performance than the imbalanced dataset, but it was not the best as compared to some intermediate ratio. By experimenting using Synthetic Minority Oversampling Technique (SMOTE) at different balancing ratios, we obtained the best results by achieving sensitivity of 92.1%, specificity value of 94.7%, overall accuracy of 93.4%, MCC of 0.869, and AUC of 0.982 with boosted random forest algorithm. This study also identifies the most discriminating features by applying the feature ranking algorithm. From the results of current experiments, it can be inferred that the performance of machine learning algorithms for the classification tasks can be enhanced by selecting optimally balanced training dataset, which can be obtained by suitably modifying the class distribution. © 2017 World Scientific Publishing Company.
Enhanced prediction for piezophilic protein by incorporating reduced set of amino acids using fuzzy-rough feature selection technique followed by SMOTE
(Springer New York LLC, 2018) Anoop Kumar Tiwari; Shivam Shreevastava; Karthikeyan Subbiah; Tanmoy Som
In this paper, the learning performance of different machine learning algorithms is investigated by applying fuzzy-rough feature selection (FRFS) technique on optimally balanced training and testing sets, consisting of the piezophilic and nonpiezophilic proteins. By experimenting using FRFS technique followed by Synthetic Minority Over-sampling Technique (SMOTE) at optimal balancing ratios, we obtain the best results by achieving sensitivity of 79.60%, specificity of 74.50%, average accuracy of 77.10%, AUC of 0.841, and MCC of 0.542 with random forest algorithm. The ranking of input features according to their differentiating ability of piezophilic and nonpiezophilic proteins is presented by using fuzzy-rough attribute evaluator. From the results, it is observed that the performance of classification algorithms can be improved by selecting the reduced optimally balanced training and testing sets. This can be obtained by selecting the relevant and non-redundant features from training sets using FRFS approach followed by suitably modifying the class distribution. © Springer Nature Singapore Pte Ltd. 2018.
Enhanced Prediction of Animal Toxins using Intuitionistic Fuzzy Rough Feature Selection Technique followed by SMOTE
(Institute of Electrical and Electronics Engineers Inc., 2021) Pankhuri Jain; Anoop Kumar Tiwari; Tanmoy Som
The toxins found in venomous animals are small peptides of disulphide-rich class. These toxins are widely utilized as therapeutic agents and pharmacological tools in medicine due to their high specificity for targets. Prediction of these toxin proteins is an interesting research area for the pharmacological and therapeutic researchers. Various machine learning techniques can offer an efficient and effective way to solve such problems. Three aspects namely: feature selection, class imbalance, and selection of appropriate learning algorithms, play the vital role in enhancing the prediction performance. In this paper, we present a new methodology to improve the prediction performance of animal toxin proteins that not only selects optimal feature subsets but also prevents misclassification occurring due to noise. Firstly, intuitionistic fuzzy rough set based feature selection technique is employed that fits the data well and prevents misclassification using atom search heuristic. Then, SMOTE (Synthetic minority oversampling technique) is applied as an oversampling technique to convert imbalanced datasets into optimally balanced datasets. Moreover, various learning algorithms are applied on the reduced optimally balanced dataset of the toxin. An accuracy of 89.2% is achieved by RealAdaBoost with RandomForest classifier. From the experimental results, it can be visualized that proposed methodology has significantly enhanced prediction performance and is outperforming the existing models. Keywords: Feature Selection, Imbalanced Dataset, SMOTE and Intuitionistic Fuzzy Rough Set. © 2021 IEEE.
Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection
(Springer Science and Business Media Deutschland GmbH, 2021) Pankhuri Jain; Anoop Kumar Tiwari; Tanmoy Som
Tuberculosis is one of the leading causes of millions of deaths across the world, mainly due to growth of drug-resistant strains. Anti-tubercular peptides may facilitate an alternate way to combat antibiotic tolerance. This study describes a novel approach for enhancing the prediction of anti-tubercular peptides by feature extraction from sequence of the peptides, selection of optimal features from the extracted features, and selection of suitable learning algorithm. Firstly, we extract different sequence features by using iFeature web server. Then, the optimal features are obtained by using a novel divergence measure-based intuitionistic fuzzy rough sets-assisted feature selection technique. Furthermore, an attempt has been made to develop models using different machine learning techniques for enhancing the prediction of anti-tubercular (or anti-mycobacterial peptides) with other antibacterial peptides (ABP) as well non-antibacterial peptides (non-ABP). Moreover, the best prediction result is obtained by vote-based classifier. Using 80:20 percentage split, the proposed method performs well, with sensitivity of 92.0%, 96.4%, specificity of 83.3%, 88.4%, overall accuracy of 87.80%, 92.90%, Mathews correlation coefficient of 0.757, 0.857, AUC of 0.922, 0.914, and g-means of 87.5%, 92.3% for anti-tubercular and ABP (primary dataset), anti-tubercular and non-ABP (secondary dataset), respectively. Finally, we have evaluated the performances of different machine learning algorithms by using the reduced training sets as produced by our proposed feature selection technique as well as already existing intuitionistic fuzzy rough set based and ensemble feature selection technique. Moreover, the performance of our proposed approach is evaluated on few benchmark and AMP datasets. From the experimental results, it can be observed that our proposed method is outperforming the previous methods. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Enhanced Prediction of plant virus-encoded RNA silencing suppressors by incorporating Reduced Set of Sequence Features using SMOTE followed by Fuzzy-Rough Feature Selection Technique
(Institute of Electrical and Electronics Engineers Inc., 2019) Pankhuri Jain; Anoop Kumar Tiwari; Tanmoy Som
Plant viruses are observed to be the natural focuses of RNA silencing. Highly diverse silencing suppressor proteins have been developed by them due to their counter defensive strategy. Silencing suppressor proteins groups share very low sequence as well as structural similarities among them. Therefore, these proteins obstruct their annotation using sequence similarity-based search techniques. Machine learning techniques can offer an alternative and effective way to solve this problem. However, the optimal performance through machine learning based techniques is being predominantly affected by diverse factors, such as availability of irrelevant and/or redundant features, class imbalance, and selection of suitable learning algorithm. In the current study, we present a novel approach to improve the prediction performance for the RNA silencing suppressors by using fuzzy rough feature selection technique with rank as well as evolutionary search on optimally balanced dataset. From the experimental results, it is obvious that fuzzy rough feature selection technique with evolutionary search on optimally balanced dataset by Synthetic Minority Over-sampling Technique (SMOTE) produces the best results with the sensitivity of 98.90%, specificity of 95.30%, overall accuracy of 96.60%, AUC of 0.993, and MCC of 0.934 using boosted random forest algorithm. On the basis of conducted experimental results, it can be observed that the proposed technique is producing the best results till date. These results can be achieved by suitably modifying the class distribution by using SMOTE followed by choosing the relevant and non-redundant features from training sets using fuzzy rough feature subset selection with evolutionary search. © 2019 IEEE.
Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis
(Elsevier Ltd, 2024) Ravi Kant; Brijesh Kumar; S.P. Maurya; Raghav Singh; Anoop Kumar Tiwari
The present study integrates various local and global optimization techniques together to estimate subsurface properties from post-stack seismic data and compare their efficacy qualitatively and quantitatively. Specifically, a local gradient-based optimization method, the quasi-newton method (QNM), is combined with global techniques such as simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO). These are well-established methods in geophysics. The research compares three global optimization methods (SA, GA, and PSO), their hybrid variants, and QNM for estimating subsurface acoustic impedance. The goal is to assess the trade-offs between solution accuracy and convergence efficiency, offering insights into the strengths and weaknesses of each approach. The objective is to guide the selection of the most effective optimization technique for seismic inversion, balancing quality and computational performance. Both synthetic and real seismic datasets are used to validate the proposed methodology, demonstrating its robust performance across various geological scenarios. Comparative analyses with single global inversion approaches reveal that hybrid optimization methods offer greater accuracy and reliability, positioning them as versatile tools for subsurface characterization. The results indicate that while the hybrid PSO method does not provide significant improvements over single PSO, it extends the convergence time. On the other hand, SA and GA perform adequately, but their hybrid versions considerably enhance solution quality at the cost of longer convergence times. Among the methods, SA shows the fastest convergence to the global solution, followed by GA and PSO. Hybrid SA stands out, delivering superior resolution and faster convergence compared to hybrid PSO and GA. © 2024
New approaches to intuitionistic fuzzy-rough attribute reduction
(IOS Press, 2018) Anoop Kumar Tiwari; Shivam Shreevastava; K.K. Shukla; Karthikeyan Subbiah
Technological advancement in the area of computing has led to production of huge amount of structured as well as unstructured data. This high dimensional data is very complex to process. Feature selection is one of the widely used techniques for preprocessing of this huge data in predictive analytics. Rough set based feature selection is an approach for handling the vagueness in data and works fine on discrete data but struggles in the continuous case as it requires discretization. This process of discretization leads to information loss. Solution for this problem was given by various authors in form of fuzzy rough set as well as intuitionistic fuzzy rough set based approaches for feature selection. Intuitionistic fuzzy set has certain benefits over the theory of traditional fuzzy sets such as its ability in a better expression of underlying information as well as its aptness to recite fragile ambiguities of the uncertainty of the objective world. The benefits offered by Intuitionistic fuzzy sets is due to the concurrent contemplation of positive, negative and hesitancy degrees for an object to belong to a set. In this paper, three novel approaches of feature reduction based on intuitionistic fuzzy rough set are presented. For this, a new intuitionistic fuzzy rough set model is established by defining a pair of lower and upper approximations. Furthermore, three new approaches of feature selection based on the degree of dependency by using score function, membership grade and cardinality of intuitionistic fuzzy numbers are introduced. Moreover, the basic results on lower and upper approximations based on rough sets are extended for intuitionistic fuzzy rough sets and analogous results are established. Moreover, a suitable algorithm is given based on our proposed approaches. Finally, the proposed algorithm is applied to an arbitrary example data set and comparison has been made with the previous fuzzy rough set based technique. The proposed algorithm is found to be better performing in terms of selected features. © 2018-IOS Press and the authors. All rights reserved.
Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study
(Nature Research, 2024) Ravi Kant; S.P. Maurya; K.H. Singh; Kottakkaran Sooppy Nisar; Anoop Kumar Tiwari
Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗ g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88% more time than the latter. © The Author(s) 2024.
Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction
(Elsevier Ltd, 2018) Anoop Kumar Tiwari; Shivam Shreevastava; Tanmoy Som; K.K. Shukla
Due to technological advancement and the explosive growth of electrically stored information, automated methods are required to aid users in maintaining and processing this huge amount of information. Experts, as well as machine learning processes on large volumes of data, are the main sources of knowledge. Knowledge extraction is an important step in framing expert and intelligent systems. However, the knowledge extraction phase is very slow or even impossible due to noise and large size of data. To enhance the productivity of machine learning algorithms, feature selection or attribute reduction plays a key role in the selection of relevant and non-redundant features to improve the performance of classifiers and interpretability of data. Many areas like machine learning, image processing, data mining, natural language processing and Bioinformatics, etc., which have high relevancy to expert and intelligent systems, are applications of feature selection. Rough set theory has been successfully applied for attribute reduction, but this theory is inadequate in the case of attribute reduction of real-valued data set as it may lose some information during the discretization process. Fuzzy and rough set theories have been combined and various attribute selection techniques were proposed, which can easily handle the real-valued data. An intuitionistic fuzzy set possesses a strong ability to represent information and better describing the uncertainty when compared to the classical fuzzy set theory as it considers positive, negative and hesitancy degree simultaneously for an object to belong to a set. This paper proposes a novel mechanism of attribute selection using tolerance-based intuitionistic fuzzy rough set theory. For this, we present tolerance-based intuitionistic fuzzy lower and upper approximations and formulate a degree of dependency of decision features over the set of conditional features. Moreover, the basic results on lower and upper approximations based on rough sets are extended for intuitionistic fuzzy rough sets and analogous results are established. In the end, the proposed algorithm is applied to an example data set and the comparison between tolerance-based fuzzy rough and intuitionistic fuzzy rough sets approaches for feature selection is presented. The proposed concept is found to be better performing in the form of selected attributes. © 2018 Elsevier Ltd