DCFS predicts stock indices using deep learning and fuzzy systems.
problem Predicting stock indices with high-dimensional inputs.
method Bottom-up layer-by-layer design of DCFS, parameter sharing.
result DCFS models outperform weak estimators and real-world data.
MBGD-RDA trains TSK fuzzy systems efficiently on big datasets.
problem Training TSK fuzzy systems efficiently on big datasets.
method MBGD-RDA combines mini-batch gradient descent, regularization, AdaBound, and novel techniques for TSK fuzzy systems.
result MBGD-RDA achieves fast convergence and superior generalization on big datasets.
This paper explores fuzzy systems' equivalence to neural networks and other machine learning methods.
problem Designing optimal fuzzy systems and overcoming challenges.
method Comparative analysis of Takagi-Sugeno-Kang fuzzy systems with neural networks, mixture of experts, CART, and stacking ensemble regression.
result Functional equivalence between fuzzy systems and machine learning methods.
FRULER learns simple fuzzy rules for regression problems.
problem Learning accurate and simple fuzzy rules for regression.
method Three-stage process: instance selection, multi-granularity discretization, evolutionary learning with Elastic Net regularization.
result FRULER produces the most accurate and simple models compared to other methods.
A fuzzy recommender system using PageRank for community detection.
problem Improving recommendation systems for large user communities.
method Fuzzy community detection using personalized PageRank.
result The method outperforms recent recommender systems on MovieLens and FilmTrust datasets.
Enhanced fuzzy system predicts chaotic time series with improved accuracy.
problem Forecasting chaotic time series with high uncertainty.
method Combines evolving fuzzy systems, participatory learning, KRLS, and type-2 fuzzy sets.
result Proposed model outperforms other methods in accuracy and complexity.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
TSK-Streams learns fuzzy rules from data streams.
problem Adaptive learning from evolving data streams.
method Combines AMRules principles with fuzzy rule advantages.
result TSK-Streams performs highly competitively in experiments.
Paper optimizes TSK fuzzy systems for large datasets with MBGD and novel regularization.
problem Optimizing TSK fuzzy systems for large datasets with high dimensionality.
method Proposes MBGD with UR and BN for TSK fuzzy classifiers.
result UR and BN improve classification performance on various UCI datasets.
A method for concise fuzzy system modeling using ESSC-SL-CTSK-FS.
problem Complex nonlinear systems with high-dimensional data and large numbers of rules.
method Integrating ESSC for antecedents and SL for consequent parameters optimization.
result Effective reduction in the number of fuzzy rules for clearer and more interpretable models.
Improved TSK fuzzy regression models with MBGD-RDA and rule pruning.
problem Training and optimizing TSK fuzzy systems for regression tasks.
method MBGD-RDA algorithm with rule pruning for TSK fuzzy systems.
result Proposed MBGD-RDA variants and rule pruning improve performance and flexibility.
A fuzzy expert system selects stocks for BSE using AI techniques.
problem Selecting stocks for investment allocation is challenging due to many influencing factors.
method Dempster-Shafer (DS) evidence theory for rule base generation, portfolio optimization model with ACO algorithm.
result The model's performance is satisfactory for short-term investment.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
MTGA optimizes multiple tasks with auxiliary data.
problem Optimizing multiple tasks simultaneously.
method Evolutionary multi-tasking genetic algorithm (MTGA).
result MTGA outperforms other approaches in optimization.
A system predicts stock prices and recommends investment portions.
problem Optimizing stock investment decisions based on predicted prices and risk tolerance.
method Support Vector Regression for price prediction, Markowitz portfolio theory and fuzzy logic for investment recommendations.
result Experimental results on NYSE show the system's effectiveness.
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
Bayesian learning with fuzzy inference for data analysis.
problem Data analysis and knowledge extraction from complex data.
method Rule-based fuzzy inference systems with Bayesian estimation and MCMC.
result Effective for regression and classification tasks, especially in financial services.
Introduces fuzzy layers to enhance deep learning performance.
problem Improving deep learning accuracy for complex tasks.
method Incorporates fuzzy methodologies into deep learning architecture.
result Preliminary results show promising performance in semantic segmentation.
KEEL improves causal discovery with fuzzy knowledge and complex data.
problem Challenges in causal discovery due to prior knowledge, domain inconsistencies, and small sample sizes.
method Weakly-supervised fuzzy knowledge and data co-driven causal discovery method (KEEL).
result KEEL outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency.
Paper simulates LR fuzzy intervals with interval-valued cores.
problem Generating random fuzzy intervals with interval-valued cores.
method Developed algorithms for simulating LR fuzzy numbers with interval-valued cores.
result Numerically efficient algorithm for simulating fuzzy values.
CFM-BD builds interpretable fuzzy models for Big Data.
problem Maintaining accuracy and interpretability in fuzzy models for Big Data.
method Distributed learning algorithm with three stages: pre-processing, rule induction, and rule selection.
result CFM-BD constructs simpler models with fewer rules and linguistic labels, achieving competitive accuracy.
Paper presents a breast cancer detection model using ELM-RBF.
problem Detecting breast cancer using mammography with high cost and side effects.
method Multilayer fuzzy expert system with ELM-RBF model.
result ELM-RBF model outperforms linear-SVM model in accuracy, precision, sensitivity, specificity, and other metrics.
In this paper, we have tried to apply the concepts of fuzzy sets to Lie groups and its relative concepts. First, we define a C1 fuzzy submanifold after reviewing C1−fuzzy manifold definition. In main section, we defined the Lie group and some its relative concepts such as fuzzy transformation group,…
Paper proposes an online learning algorithm for a neuro-fuzzy classifier with mixed data.
problem Inability of GFMMNN learning algorithms to handle mixed-attribute data.
method Extended online learning algorithm for GFMMNN that can handle both continuous and categorical features.
result Superior and stable classification performance compared to other learning algorithms.
EGFC learns from streaming data to classify power quality disturbances.
problem Real-time detection and classification of power quality disturbances.
method Evolving Gaussian Fuzzy Classification (EGFC) framework with semi-supervised learning.
result Encouraging classification results from online data streams.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
Paper defines kernels for fuzzy sets, useful in machine learning.
problem Uncertainty in data with fuzzy set membership.
method Defined cross product, intersection, non-singleton, and distance-based kernels.
result Kernels improve machine learning tasks with fuzzy data.
iDriveSense offers safer trip recommendations by considering road anomalies.
problem Drivers seek safer routes despite shortest/fastest path recommendations.
method Crowdsensing, vehicle sensors, fuzzy systems for road quality assessment.
result Proposes a system for dynamic route planning considering road anomalies.
Proposes a fuzzy rule-based method for data visualization.
problem Preserving neighborhood relationships and handling non-linear manifolds in data visualization.
method Uses a first-order Takagi-Sugeno model with clusters and Geodesic c-means clustering for rule generation and parameter estimation.
result Behaves desirably and performs better than or comparable to other methods.
ANFIS system improves satellite attitude estimation and control.
problem Improving accuracy of satellite attitude estimation and control.
method Adaptive neuro-fuzzy integrated (ANFIS) model for satellite attitude estimation and control.
result ANFIS controller outperforms optimal PID controller in time and smoothness.
A new adaptive binarization technique using fuzzy integrals improves image quality.
problem Improving image thresholding quality.
method FLAT (Fuzzy Local Adaptive Thresholding) based on fuzzy integrals.
result The proposed FLAT method produces better image quality than traditional algorithms and neural networks.
Paper investigates differentiable fuzzy implications and their suitability for learning.
problem Analyzing differentiable fuzzy implications and their suitability for learning.
method Investigates the properties of fuzzy implications in a differentiable setting and introduces a new family of fuzzy implications.
result Various fuzzy implications are unsuitable for differentiable learning, and a new family of fuzzy implications is introduced.
Fuzzy hashes learn from data to improve file similarity detection.
problem Measuring similarity between files, especially malware.
method Learned fuzzy hashes using a minimax training framework.
result Learned fuzzy hashes outperform traditional methods for file similarity.
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
problem Lack of supervised clustering for TSK fuzzy classifiers.
method SESSC integrates within-cluster compactness, between-cluster separation, and label information.
result SESSC initialization outperforms other clustering methods, especially for small rule numbers.
This paper uses fuzzy C-Means clustering and sonification to analyze heart rate variability.
problem Identifying suitable features from HRV analysis for sonification.
method Unsupervised machine learning (fuzzy C-Means clustering) and sonification techniques.
result Improves sonification interpretability by selecting appropriate HRV features.
A new fuzzy clustering method using hyperbolic smoothing for large datasets.
problem Building fuzzy clusters for large data sets efficiently.
method A novel smoothing numerical approach to relax the sum-of-squares criterion, converting the problem into a differentiable optimization problem.
result The method produces better fuzzy partitions compared to traditional fuzzy C-means. New method reduces complexity of fuzzy decision trees in Big Data.
problem Reducing complexity in multi-way fuzzy decision trees for Big Data classification.
method Two-step process: 1) Probability integral transform, 2) Ruspini strong fuzzy partition.
result Up to 6 million fewer leaves with similar classification accuracy.
Fuzzy ROC handles indeterminate data points in ROC analysis.
problem Handling indeterminate data points in ROC analysis.
method Extends ROC visualization to indeterminate data, addressing sensitivity and specificity bounds.
result Visual summarization of possibilities from different indeterminacy zones.
Unified probabilistic foundation for fuzzy simplicial sets in dimensionality reduction.
problem Lack of clear probabilistic interpretation in fuzzy simplicial sets.
method Introducing a probabilistic framework explaining fuzzy simplicial sets as marginals of probability measures on simplicial sets.
result Unified probabilistic theoretical foundation for fuzzy simplicial sets.
Fuzzy clustering reduces text data dimensions effectively.
problem Sparsity and high dimensionality in document-term frequency matrices.
method Fuzzy clustering applied as a new unsupervised feature transformation method.
result Fuzzy clustering outperforms PCA and SVD in reducing text data dimensions.
New algorithm for fuzzy clustering improves clustering quality.
problem Seeding iterative fuzzy algorithms for high-quality clustering.
method MaxMin Linear initialization algorithm for fuzzy C-Means.
result Validation through experiments on various datasets.
FSL-BM improves real-time classification with fuzzy logic and binary meta-features.
problem Real-time classification accuracy, memory consumption, and time complexity.
method FSL-BM integrates fuzzy logic, binary meta-features, Hamming Distance, and Hash function for efficient supervised learning.
result FSL-BM provides faster and more accurate real-time classification compared to existing algorithms.
Fuzzy Bayesian Learning uses model evidence to choose best rule base.
problem Selecting the best fuzzy rule base among alternatives.
method Calculates marginal likelihood to compare models.
result Marginal likelihood provides a better model selection than MSE.
Improved fuzzy support vector machine for stock price trend forecasting.
problem Weak performance of traditional support vector machines in handling fuzzy and noisy data.
method Proposed a novel advanced fuzzy support vector machine (NA-FSVM) to improve precision.
result Improved model precision in predicting stock price trends.
Proposes new random models for fuzzy clustering similarity measures.
problem Challenges in choosing a random model for fuzzy clustering similarity measures.
method Introduces three intuitive and explainable random models for fuzzy clusterings.
result Each random model has distinct behavior, emphasizing the importance of accurate model selection.
Optimized fuzzy entropy framework improves feature selection and classification performance.
problem Improving feature selection and classification in fuzzy entropy frameworks.
method Implemented and compared combinations of ideal vectors, maximal similarity classifiers, and fuzzy entropy functions.
result Optimized combination of ideal vector, similarity classifier, and fuzzy entropy function achieved the most stable performance for all three datasets.
Generates music with video emotion using deep neural networks.
problem Generating music with video emotion.
method Hybrid deep neural network combining ANFIS and LSTM.
result Low mean absolute errors and similar global features in spectrograms.
Study compares GFMM neural networks for pattern classification.
problem Pattern classification problems.
method Two algorithms (incremental and agglomerative learning) to train GFMM neural networks.
result GFMM neural networks show strong and weak points on benchmark datasets.