Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing multiple feature views effectively. To address these limitations we propose the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model. The proposed approach comprises three key components: intuitionistic fuzzy sets for uncertainty handling, graph embedding to capture intrinsic geometric structures, and multiview learning to use complementary information from multiple feature spaces. The model assigns intuitionistic fuzzy membership and non-membership values to data points making it robust to outliers. Also, the graph embedding framework preserves topological structures, increasing the generalization performance. We performed experiments on benchmark datasets from UCI and KEEL repositories which concludes that IFGRVFL-MV outperforms existing models in classification accuracy. Our results establish that IFGRVFL-MV is a promising advancement in the domain of uncertainty and multiview environments.

Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To this end, we aim to address the problem by annotating a large-scale ‘in-the-wild’ dataset for iden-tifying human perceptions about the ‘Most Important Person (MIP)‘ in an image. The paper provides a thorough description of our proposed Multimodal Large Language Model (MLLM) based data annotation strategy, and a thor-ough data quality analysis. Further, we perform a comprehensive benchmarking of the proposed dataset utilizing state-of-the-art MIP localization methods, indicating a significant drop in performance compared to existing datasets. The performance drop shows that the existing MIP localization algorithms must be more robust with respect to ‘in-the-wild’ situations. We believe the proposed dataset will play a vital role in building the next-generation social situation understanding methods. The dataset and associated code will be made available for research purposes.

Automatic identification of emotions is important in human-centered computing. It allows machines to better understand user emotions. Identifying emotions via neural sensing techniques such as electroencephalogram (EEG) is a promising approach. In this paper, we aim to identify the emotions class from EEG signals. We frame emotion identification as a classification task and apply spectral and statistical encoders to extract the relevant features. We validate our approach on EmoNeuroDB dataset. Our method outperforms the EmoNeuroDB baseline, achieving a 42.10% increase in class prediction accuracy.

Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have been proposed for the diagnosis of AD at early stage. Recently, deep learning architectures have received quite a lot attention. Most of the deep learning architectures suffer from the issues of local minima, slow convergence and sensitivity to learning rate. To overcome these issues, non-iterative learning based deep randomized models especially random vector functional link network (RVFL) with direct links have proven to be successful. However, deep RVFL and its ensemble models are trained only on normal samples. In this paper, deep RVFL and its ensembles are enabled to incorporate privileged information, as the standard RVFL model and its deep models are unable to use privileged information. To fill this gap, we have incorporated learning using privileged information (LUPI) in deep RVFL model, and propose deep RVFL with LUPI framework (dRVFL+). Privileged information is available while training the models. As RVFL is an unstable classifier, we propose ensemble deep RVFL+ with LUPI framework (edRVFL+) which exploits the LUPI as well as the diversity among the base leaners for better classification. Unlike traditional ensemble approach wherein multiple base learners are trained, the proposed edRVFL+ model optimises a single network and generates an ensemble via optimization at different levels of random projections of the data. Both dRVFL+ and edRVFL+ efficiently utilise the privileged information which results in better generalization performance. In LUPI framework, half of the available features are used as normal features and rest as the privileged features. However, we propose a novel approach for generating the privileged information. We utilise different activation functions while processing the normal and privileged information in the proposed deep architectures. To the best of our knowledge, this is first time that a separate privileged information is generated. The proposed dRVFL+ and edRVFL+ models are employed for the diagnosis of Alzheimer's disease. Experimental results demonstrate the superiority of the proposed dRVFL+ and edRVFL+ models over baseline models. Thus, the proposed edRVFL+ model can be utilised in clinical setting for the diagnosis of AD.

Random vector functional link (RVFL) network has been successfully employed in diverse domains such as computer vision and machine learning, due to its universal approximation capability. Recently, the shallow RVFL architecture has been extended to deep architectures. In deep architectures, multiple hidden layers are stacked for extracting informative features from the original feature space. Therefore, having rich features, deep models are very successful compared to shallow models. In this article, we propose an extended feature RVFL (efRVFL) model that is trained over extended feature space generated analytically from the original feature space. The proposed efRVFL model has three types of features, i.e., original features, supervised randomized (newly generated) features, and unsupervised randomized features, in its feature matrix. The proposed efRVFL model with additional features has capability to capture nonlinear hidden relationships within the dataset. The proposed efRVFL model is an unstable classifier, and thus, its performance can be improved further via ensemble learning. Ensemble models are stable and accurate and have better generalization performance than single models. Therefore, we also propose an ensemble of extended feature RVFL (en-efRVFL) model. Each base model of en-efRVFL is trained over different feature spaces so that more accurate and diverse base models can be generated. The outcome of the base models is integrated via average voting scheme. Empirical evaluation over$46$UCI classification datasets demonstrates that the proposed efRVFL and en-efRVFL models have better performance than RVFL and other given deep models. Furthermore, the experimental results over$12$sparse datasets show that the proposed en-efRVFL model has a winning performance among several deep feedforward neural networks (FNNs).

Alzheimer's disease (AD) is a prominent neurodegenerative disorder, which leads to memory loss and cognitive impairment. The progression is irreversible and shows atrophies in cerebral cortex. Multiple studies revealed that the early diagnosis and early treatment can slow the progress of dementia, and hence, further atrophies can be controlled. Brain imaging data, such as magnetic resonance imaging (MRI), have been prominently used for the diagnosis of AD. Multiple approaches have been proposed for the diagnosis of AD. We propose a novel intuitionistic fuzzy random vector functional link network (IFRVFL) for the diagnosis of AD. Unlike standard random vector functional link (RVFL) network, extreme learning machine (ELM), and kernel ridge regression (KRR), which uses a uniform weighting approach for generating the optimal classifier, the proposed IFRVFL uses a fuzzy weighted approach for generating the optimal classifier. A uniform weighting scheme assumes that all the data samples are equally important; however, in real-world scenarios, this assumption may not hold true due to the presence of outliers and noise. Hence, it results in lower generalization. The proposed IFRVFL assigns each sample an intuitionistic fuzzy number (IFN), which is a function of membership and nonmembership score of a sample. The membership score is a function of the sample distance from the centroid of its corresponding class and the nonmembership score is a function of sample distance from the centroid as well as the neighborhood of the given sample. The proposed IFRVFL effectively reduces the influence of outliers. To evaluate the efficiency of the proposed IFRVFL model, we employed it for the diagnosis of AD. Experimental results demonstrate that the proposed IFRVFL model is superior in mild cognitive impairment (MCI) versus AD case. Thus, IFRVFL can be used in the clinical setting for the early diagnosis of AD. Furthermore, to check the robustness of the proposed IFRVFL model, we also evaluated it on benchmark datasets. Experimental results and the statistical tests reveal that the proposed IFRVFL is better in comparison to baseline models. The source code of the proposed IFRVFL formulation is available at https://github.com/mtanveer1/.

The domain of machine learning is confronted with a crucial research area known as class imbalance (CI) learning, which presents considerable hurdles in the precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. To overcome this limitation, we propose a novel graph-embedded intuitionistic fuzzy RVFL for CI learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model offers a plethora of benefits: 1) leveraging graph embedding (GE) to preserve the inherent topological structure of the datasets; 2) employing intuitionistic fuzzy (IF) theory to handle uncertainty and imprecision in the data; and 3) the most important, it tackles CI learning. The amalgamation of a weighting scheme, GE, and IF sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the CI issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuitionistic fuzzy TWSVM combined the concept of intuitionistic fuzzy number with TWSVMs to reduce the effect of outliers and distinguish support vectors from noise. Despite these benefits, TWSVMs and intuitionistic fuzzy TWSVMs still suffer from some drawbacks as: 1) the local neighborhood information is ignored among the data points and 2) they solve quadratic programming problems (QPPs), which is computationally inefficient. To overcome these issues, we propose a novel intuitionistic fuzzy weighted least squares TWSVMs for classification problems. The proposed approach uses local neighborhood information among the data points and also uses both membership and nonmembership weights to reduce the effect of noise and outliers. The proposed approach solves a system of linear equations instead of solving the QPPs which makes the model more efficient. We evaluated the proposed intuitionistic fuzzy weighted least squares TWSVMs on several benchmark datasets to show the efficiency of the proposed model. Statistical analysis is done to quantify the results statistically. As an application, we used the proposed model for the diagnosis of Schizophrenia disease.

Alzheimer’s disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of the abnormal brain. Machine learning models like support vector regression (SVR) based models have been successfully employed in the estimation of brain age. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR),$\varepsilon$-TSVR, and Lagrangian TSVR (LTSVR) models have been used for the regression problems.$\varepsilon$-TSVR and LTSVR models seek a pair of$\varepsilon$-insensitive proximal planes for generation of end regressor. However, SVR and TSVR based models have several drawbacks- i) SVR model is computationally inefficient compared to the TSVR based models. ii) Twin SVM based models involve the computation of matrix inverse which is intractable in real world scenario’s. iii) Both TSVR and LTSVR models are based on empirical risk minimization principle and hence may be prone to overfitting. iv) TSVR and LTSVR assume that the matrices appearing in their formulation are positive definite which may not be satisfied in real world scenario’s. To overcome these issues, we formulate improved least squares twin support vector regression (ILSTSVR). The proposed ILSTSVR modifies the TSVR by replacing the inequality constraints with the equality constraints and minimizes the slack variables using squares of$L_2$norm instead of$L_1$. Also, we introduce a different Lagrangian function to avoid the computation of matrix inverses. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer’s disease for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for brain-age prediction.

Machine learning models have been successfully employed in the diagnosis of Schizophrenia disease. The impact of classification models and the feature selection techniques on the diagnosis of Schizophrenia have not been evaluated. Here, we sought to access the performance of classification models along with different feature selection approaches on the structural magnetic resonance imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy control subjects. We evaluated different classification algorithms based on support vector machine (SVM), random forest, kernel ridge regression and randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the feature selection techniques. Based on the evaluation, SVM based models with Gaussian kernel proved better compared to other classification models and Wilcoxon feature selection emerged as the best feature selection approach. Moreover, in terms of data modality the performance on integration of the grey matter and white matter proved better compared to the performance on the grey and white matter individually. Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease. This indicates that proper selection of the features and the classification models can improve the diagnosis of Schizophrenia.

A teacher in a school plays significant role in classroom while teaching the students. Similarly, learning via privileged information (LUPI) gives extra information generated by a teacher to 'teach' the learning algorithm while training. This paper proposes minimum variance embedded random vector functional link network with privileged information (MVRVFL+). The proposed MVRVFL+ minimizes the intraclass variance of the training data and uses privileged information paradigm which provides the additional knowledge during the training of the model. The proposed MVRVFL+ classification model is evaluated on 43 benchmark UCI datasets. From the experimental analysis, the proposed MVRVFL+ showed best average accuracy and emerged as the lowest average rank classifier among the baseline models.

Twin support vector machines (TSVMs) have been successfully employed for binary classification problems. With the advent of machine learning algorithms, data have proliferated and there is a need to handle or process large-scale data. TSVMs are not successful in handling large-scale data due to the following: 1) the optimization problem solved in the TSVM needs to calculate large matrix inverses, which makes it an ineffective choice for large-scale problems; 2) the empirical risk minimization principle is employed in the TSVM and, hence, may suffer due to overfitting; and 3) the Wolfe dual of TSVM formulation involves positive-semidefinite matrices, and hence, singularity issues need to be resolved manually. Keeping in view the aforementioned shortcomings, in this article, we propose a novel large-scale fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL). We formulate the LS-FLSTSVM-CIL such that the proposed optimization problem ensures that: 1) no matrix inversion is involved in the proposed LS-FLSTSVM-CIL formulation, which makes it an efficient choice for large-scale problems; 2) the structural risk minimization principle is implemented, which avoids the issues of overfitting and results in better performance; and 3) the Wolfe dual formulation of the proposed LS-FLSTSVM-CIL model involves positive-definite matrices. In addition, to resolve the issues of class imbalance, we assign fuzzy weights in the proposed LS-FLSTSVM-CIL to avoid bias in dominating the samples of class imbalance problems. To make it more feasible for large-scale problems, we use an iterative procedure known as the sequential minimization principle to solve the objective function of the proposed LS-FLSTSVM-CIL model. From the experimental results, one can see that the proposed LS-FLSTSVM-CIL demonstrates superior performance in comparison to baseline classifiers. To demonstrate the feasibility of the proposed LS-FLSTSVM-CIL on large-scale classification problems, we evaluate the classification models on the large-scale normally distributed clustered (NDC) dataset. To demonstrate the practical applications of the proposed LS-FLSTSVM-CIL model, we evaluate it for the diagnosis of Alzheimer’s disease and breast cancer disease. Evaluation on NDC datasets shows that the proposed LS-FLSTSVM-CIL has feasibility in large-scale problems as it is fast in comparison to the baseline classifiers.

Machine learning (ML) algorithms play a vital role in brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals (N = 788) as a training set followed by different regression algorithms (18 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimers disease patients. The prediction accuracy in the independent test set (i.e., CH set) varied in regression algorithms (mean absolute error (MAE) from 4.63 to 7.14 yrs, R2 from 0.76 to 0.88). The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE = 4.63 yrs, R2 = 0.88, 95% CI = [-1.26, 1.42]) and Binary Decision Tree algorithm (MAE = 7.14 yrs, R2 = 0.76, 95% CI = [-1.50, 2.62]), respectively. Our experimental results demonstrate that prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.

Alzheimer's disease (AD) is one of the deadliest neurodegenerative diseases ailing the elderly population all over the world. An ensemble of Deep learning (DL) models can learn highly complicated patterns from MRI scans for the detection of AD by utilizing diverse solutions. In this work, we propose a computationally efficient, DL-architecture agnostic, ensemble of deep neural networks, named 'Deep Transfer Ensemble (DTE)' trained using transfer learning for the classification of AD. DTE leverages the complementary feature views and diversity introduced by many different locally optimum solutions reached by individual networks through the randomization of hyper-parameters. DTE achieves an accuracy of 99.05% and 85.27% on two independent splits of the large dataset for cognitively normal (NC) vs AD classification task. For the task of mild cognitive impairment (MCI) vs AD classification, DTE achieves 98.71% and 83.11% respectively on the two independent splits. It also performs reasonable on a small dataset consisting of only 50 samples per class. It achieved a maximum accuracy of 85% for NC vs AD on the small dataset. It also outperformed snapshot ensembles along with several other existing deep models from similar kind of previous works by other researchers.

In this paper, we propose ensemble of random vector functional link network known as co-trained random vector functional link network (coRVFL). Random vector functional link network solves the optimization problem via closed form solution and hence avoids the problems of slow convergence and local minima problems. The proposed coRVFL trains two RVFL models jointly such that each RVFL model is constructed with different feature projection matrix and hence, shows better generalization performance. We use randomly projected features and sparse-l1. norm autoencoder based features to train the proposed coRVFL model. Experimental results show that the proposed coRVFL is performing better in comparison with the baseline models. Furthermore, statistical analysis reveals that the proposed coRVFL model performs statistically better than the baseline approaches.

Ensemble learning methods, which combine several base classifiers, is a common technique to enhance the classification ability of ensemble models in the field of pattern recognition and machine learning. Rotation Forest, an ensemble algorithm, has been used widely in various fields with nice generalization performance. The main idea of Rotation Forest is to animate concurrently both diversity and individual accuracy within the ensemble. On the other hand, random vector functional link (RVFL) neural network, a randomized version of single layer feed-forward neural network (SLFN), is a successful model because of its universal approximation property. In this paper, we propose a novel ensemble method, known as rotated random vector functional link neural network (RoF-RVFL), which combines rotation forest (RoF) and RVFL classifiers. To verify the effectiveness of the proposed RoF- RVFL method, empirical comparisons are carried out among Rotation Forest (RoF), Random Forest (RaF), RVFL and the proposed RoF-RVFL method over 42 UCI benchmark datasets. The experimental results show that the proposed RoF-Rvflmethod is able to generate more robust network with better generalization performance.

Twin support vector machines (TWSVM) have been successfully applied to the classification problems. TWSVM is computationally efficient model of support vector machines (SVM). However, in real world classification problems issues of class imbalance and noise provide great challenges. Due to this, models lead to the inaccurate classification either due to higher tendency towards the majority class or due to the presence of noise. We provide an improved version of robust fuzzy least squares twin support vector machine (RFLSTSVM) known as regularized robust fuzzy least squares twin support vector machine (RRFLSTSVM) to handle the imbalance problem. The advantage of RRFLSTSVM over RFLSTSVM is that the proposed RRFLSTSVM implements the structural risk minimization principle by the introduction of regularization term in the primal formulation of the objective functions. This modification leads to the improved classification as it embodies the marrow of statistical learning theory. The proposed RRFLSTSVM doesn't require any extra assumption as the matrices resulting in the dual are positive definite. However, RFLSTSVM is based on the assumption that the inverse of the matrices resulting in the dual always exist as the matrices are positive semi-definite. To subsidize the effects of class imbalance and noise, the data samples are assigned weights via fuzzy membership function. The fuzzy membership function incorporates the imbalance ratio knowledge and assigns appropriate weights to the data samples. Unlike TWSVM which solves a pair of quadratic programming problem (QPP), the proposed RRFLSTSVM method solves a pair of system of linear equations and hence is computationally efficient. Experimental and statistical analysis show the efficacy of the proposed RRFLSTSVM method.

In this paper, we propose an improved version of sparse pinball twin support vector machine (SPTSVM) [1], called improved sparse pinball twin support vector machine (ISPTSVM). SPTSVM implements empirical risk minimization principle and the matrices appearing in the formulation of SPTSVM are positive semi-definite. Here, we reformulate the primal problems of SPTSVM by introducing extra regularization term to the objective function of SPTSVM. Unlike SPTSVM, structural risk minimization (SRM) principle is implemented in the proposed ISPTSVM which embodies the marrow of statistical learning theory. Also, the matrices that appear in the dual formulation of the proposed ISPTSVM are positive definite. Results computed on multiple UCI benchmark datasets clearly indicate the effectiveness and applicability of the proposed ISPTSVM compared to pinball support vector machine (Pin-SVM), twin bounded support vector machine (TBSVM) and SPTSVM.