Search Results for author: M. Tanveer

Found 20 papers, 4 papers with code

HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function

no code implementations30 Jan 2024 Mushir Akhtar, M. Tanveer, Mohd. Arshad

It is worth noting that the HawkEye loss function stands out as the first loss function in SVR literature to be bounded, smooth, and simultaneously possess an insensitive zone.

regression

LSTSVR-PI: Least square twin support vector regression with privileged information

no code implementations5 Dec 2023 Anuradha Kumari, M. Tanveer

Moreover, as an application, we conduct experiments on time series datasets, which results in the superiority of the proposed LSTSVR-PI.

Learning Theory regression

Support matrix machine: A review

no code implementations30 Oct 2023 Anuradha Kumari, Mushir Akhtar, Rupal Shah, M. Tanveer

However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors.

Multi-class Classification

Projection based fuzzy least squares twin support vector machine for class imbalance problems

no code implementations27 Sep 2023 M. Tanveer, Ritik Mishra, Bharat Richhariya

Therefore, to remove these problems we propose a novel fuzzy based approach to deal with class imbalanced as well noisy datasets.

RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning

1 code implementation5 Sep 2023 Mushir Akhtar, M. Tanveer, Mohd. Arshad

In the domain of machine learning algorithms, the significance of the loss function is paramount, especially in supervised learning tasks.

EEG Electroencephalogram (EEG)

Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers

no code implementations15 Jul 2023 M. Sajid, A. K. Malik, M. Tanveer

To address this issue, we propose the fuzzy BLS (F-BLS) model, which assigns a fuzzy membership value to each training point to reduce the influence of noises and outliers.

Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning

1 code implementation15 Jul 2023 M. A. Ganaie, M. Sajid, A. K. Malik, M. Tanveer

To overcome this limitation, we propose a novel graph embedded intuitionistic fuzzy RVFL for class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets.

Graph Embedding

Heterogeneous Oblique Double Random Forest

no code implementations13 Apr 2023 M. A. Ganaie, M. Tanveer, I. Beheshti, N. Ahmad, P. N. Suganthan

Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node.

Deep Learning for Brain Age Estimation: A Systematic Review

no code implementations7 Dec 2022 M. Tanveer, M. A. Ganaie, Iman Beheshti, Tripti Goel, Nehal Ahmad, Kuan-Ting Lai, Kaizhu Huang, Yu-Dong Zhang, Javier Del Ser, Chin-Teng Lin

In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data.

Age Estimation

Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization

1 code implementation18 Sep 2022 Ye Shi, Leijie Zhang, Zehong Cao, M. Tanveer, Chin-Teng Lin

In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR.

regression

Biceph-Net: A robust and lightweight framework for the diagnosis of Alzheimer's disease using 2D-MRI scans and deep similarity learning

no code implementations23 Mar 2022 A. H. Rashid, A. Gupta, J. Gupta, M. Tanveer

Biceph-Net is also superior in performance compared to vanilla 2D convolutional neural networks (CNN) for AD diagnosis using 2D MRI slices.

Diagnosis of Schizophrenia: A comprehensive evaluation

no code implementations22 Mar 2022 M. Tanveer, Jatin Jangir, M. A. Ganaie, Iman Beheshti, M. Tabish, Nikunj Chhabra

Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease.

Classification feature selection

Random vector functional link network: recent developments, applications, and future directions

no code implementations13 Feb 2022 A. K. Malik, Ruobin Gao, M. A. Ganaie, M. Tanveer, P. N. Suganthan

To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed.

Hyperparameter Optimization

Oblique and rotation double random forest

no code implementations3 Nov 2021 M. A. Ganaie, M. Tanveer, P. N. Suganthan, V. Snasel

The oblique double random forest models are multivariate decision trees.

Comprehensive Review On Twin Support Vector Machines

no code implementations1 May 2021 M. Tanveer, T. Rajani, R. Rastogi, Y. H. Shao, M. A. Ganaie

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively.

regression

Ensemble deep learning: A review

no code implementations6 Apr 2021 M. A. Ganaie, Minghui Hu, A. K. Malik, M. Tanveer, P. N. Suganthan

Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance.

Ensemble Learning

Identification of Chimera using Machine Learning

1 code implementation16 Jan 2020 M. A. Ganaie, Saptarshi Ghosh, Naveen Mendola, M. Tanveer, Sarika Jalan

The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance.

BIG-bench Machine Learning

Random Vector Functional Link Neural Network based Ensemble Deep Learning

no code implementations30 Jun 2019 Rakesh Katuwal, P. N. Suganthan, M. Tanveer

The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed form solution as in a standard RVFL network.

Ensemble Learning

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