Bi-Level Spectral Feature Selection.

Unsupervised feature selection (UFS) aims to learn an indicator matrix relying on some characteristics of the high-dimensional data to identify the features to be selected. However, traditional unsupervised methods perform only at the feature level, i.e.

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features.

Universal Approximation Abilities of a Modular Differentiable Neural Network.

Approximation ability is one of the most important topics in the field of neural networks (NNs). Feedforward NNs, activated by rectified linear units and some of their specific smoothed versions, provide universal approximators to convex as well as continuous functions. However, most of these networks are investigated empirically, or their characteristics are analyzed based on specific operation rules.

Supervised Feature Selection via Collaborative Neurodynamic Optimization.

As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In this article, supervised feature selection is at first formulated as a mixed-integer optimization problem with an objective function of weighted feature redundancy and relevancy subject to a cardinality constraint on the number of selected features. It is equivalently reformulated as a bound-constrained mixed-integer optimization problem by augmenting the objective function with a penalty function for realizing the cardinality constraint.

Fractional Approximation of Broad Learning System.

Approximation ability is of much importance for neural networks. The broad learning system (BLS) (Chen and Liu, 2018), widely used in the industry with good performance, has been proved to be a universal approximator from the aspect of density. This kind of approximation property is very important, which proves the existence of the desired network but does not provide a means of construction that is commonly implemented through complexity aspect.

Unsupervised feature selection via adaptive autoencoder with redundancy control.

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of unlabeled high-dimensional data. We present a novel adaptive autoencoder with redundancy control (AARC) as an unsupervised feature selector. By adding two Group Lasso penalties to the objective function, AARC integrates unsupervised feature selection and determination of a compact network structure into a single framework.

Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding.

Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals.

In Search of Trustworthy and Transparent Intelligent Systems With Human-Like Cognitive and Reasoning Capabilities.

At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL)/Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance achieved by such systems. This has resulted in an enormous hope on such techniques and often these are viewed as all-cure solutions.

Feature Selection Using a Neural Network With Group Lasso Regularization and Controlled Redundancy.

We propose a neural network-based feature selection (FS) scheme that can control the level of redundancy in the selected features by integrating two penalties into a single objective function. The Group Lasso penalty aims to produce sparsity in features in a grouped manner. The redundancy-control penalty, which is defined based on a measure of dependence among features, is utilized to control the level of redundancy among the selected features.

Learning Optimized Structure of Neural Networks by Hidden Node Pruning With L Regularization.

We propose three different methods to determine the optimal number of hidden nodes based on L regularization for a multilayer perceptron network. The first two methods, respectively, use a set of multiplier functions and multipliers for the hidden-layer nodes and implement the L regularization on those, while the third method equipped with the same multipliers uses a smoothing approximation of the L regularization. Each of these methods begins with a given number of hidden nodes, then the network is trained to obtain an optimal architecture discarding redundant hidden nodes using the multiplier functions or multipliers.

Weight Noise Injection-Based MLPs With Group Lasso Penalty: Asymptotic Convergence and Application to Node Pruning.

The application and theoretical analysis of fault tolerant learning are very important for neural networks. Our objective here is to realize fault tolerant sparse multilayer perceptron (MLP) networks. The stochastic gradient descent method has been employed to perform online learning for MLPs.

Distributed Adaptive Tracking Synchronization for Coupled Reaction-Diffusion Neural Network.

This paper considers the tracking synchronization problem for a class of coupled reaction-diffusion neural networks (CRDNNs) with undirected topology. For the case where the tracking trajectory has identical individual dynamic as that of the network nodes, the edge-based and vertex-based adaptive strategies on coupling strengths as well as adaptive controllers, which demand merely the local neighbor information, are proposed to synchronize the CRDNNs to the tracking trajectory. To reduce the control costs, an adaptive pinning control technique is employed.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources.

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities.

Construction of synergy networks from gene expression data related to disease.

A few methods have been developed to determine whether genes collaborate with each other in relation to a particular disease using an information theoretic measure of synergy. Here, we propose an alternative definition of synergy and justify that our definition improves upon the existing measures of synergy in the context of gene interactions. We use this definition on a prostate cancer data set consisting of gene expression levels in both cancerous and non-cancerous samples and identify pairs of genes which are unable to discriminate between cancerous and non-cancerous samples individually but can do so jointly when we take their synergistic property into account.

Discovery of synergistic genetic network: A minimum spanning tree-based approach.

Identification of gene interactions is one of the very well-known and important problems in the field of genetics. However, discovering synergistic gene interactions is a relatively new problem which has been proven to be as significant as the former in genetics. Several approaches have been proposed in this regard and most of them depend upon information theoretic measures.

Identifying changes in EEG information transfer during drowsy driving by transfer entropy.

Drowsy driving is a major cause of automobile accidents. Previous studies used neuroimaging based approaches such as analysis of electroencephalogram (EEG) activities to understand the brain dynamics of different cortical regions during drowsy driving. However, the coupling between brain regions responding to this vigilance change is still unclear.

Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease.

Motivation: Alzheimer's disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy of AD patients following diagnosis is ∼7 years. In 2006, researchers estimated that 0.

A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification.

We present an integrated algorithm for simultaneous feature selection (FS) and designing of diverse classifiers using a steady state multiobjective genetic programming (GP), which minimizes three objectives: 1) false positives (FPs); 2) false negatives (FNs); and 3) the number of leaf nodes in the tree. Our method divides a c -class problem into c binary classification problems. It evolves c sets of genetic programs to create c ensembles.

Feature selection using a neural framework with controlled redundancy.

We first present a feature selection method based on a multilayer perceptron (MLP) neural network, called feature selection MLP (FSMLP). We explain how FSMLP can select essential features and discard derogatory and indifferent features. Such a method may pick up some useful but dependent (say correlated) features, all of which may not be needed.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary.

Sensor (group feature) selection with controlled redundancy in a connectionist framework.

For many applications, to reduce the processing time and the cost of decision making, we need to reduce the number of sensors, where each sensor produces a set of features. This sensor selection problem is a generalized feature selection problem. Here, we first present a sensor (group-feature) selection scheme based on Multi-Layered Perceptron Networks.

ASMiGA: an archive-based steady-state micro genetic algorithm.

We propose a new archive-based steady-state micro genetic algorithm (ASMiGA). In this context, a new archive maintenance strategy is proposed, which maintains a set of nondominated solutions in the archive unless the archive size falls below a minimum allowable size. It makes the archive size adaptive and dynamic.

A fuzzy-rule-based approach for single frame super resolution.

In this paper, a novel fuzzy rule-based prediction framework is developed for high-quality image zooming. In classical interpolation-based image zooming, resolution is increased by inserting pixels using certain interpolation techniques. Here, we propose a patch-based image zooming technique, where each low-resolution (LR) image patch is replaced by an estimated high-resolution (HR) patch.

A fast algorithm to compute precise type-2 centroids for real-time control applications.

An interval type-2 fuzzy set (IT2 FS) is characterized by its upper and lower membership functions containing all possible embedded fuzzy sets, which together is referred to as the footprint of uncertainty (FOU). The FOU results in a span of uncertainty measured in the defuzzified space and is determined by the positional difference of the centroids of all the embedded fuzzy sets taken together. This paper provides a closed-form formula to evaluate the span of uncertainty of an IT2 FS.

Prediction of protein secondary structure using probability based features and a hybrid system.

In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences).