arXiv research

A new hybrid fuzzy-crisp clustering algorithm addresses imbalanced cluster sizes.

A new model for detecting overlapping communities in weighted networks.

This paper introduces Bounded Fuzzy Possibilistic Method (BFPM) by addressing several issues that previous clustering/classification methods have not considered. In fuzzy clustering, object's membership values should sum to 1. Hence, any object may obtain full membership in at most one cluster. Possibilistic clustering…

BFPM improves machine learning accuracy by considering object types and memberships flexibly.

Robust fuzzy clustering for EEG driver alertness with outlier detection.

Improves k-NN for monotonic data with robustness against noise.

This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0,1]$-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. Applicabili…

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machi…

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local inf…

We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step p…

A novel fuzzy clustering method for multivariate time series.

FCM clustering adapts to persistence diagrams for topological data analysis.

FCPCA fuzzy clusters high-dimensional time series data efficiently.

A new fuzzy time series method for non-stationary data.

A new model estimates mixed memberships for categorical data with weighted responses.

Paper extends Bayes Theorem for interval probability estimates.

In this paper, we take a new look at the possibilistic c-means (PCM) and adaptive PCM (APCM) clustering algorithms from the perspective of uncertainty. This new perspective offers us insights into the clustering process, and also provides us greater degree of flexibility. We analyze the clustering behavior of PCM-based…

In this paper, we propose a new fuzzy clustering algorithm based on the mode-seeking framework. Given a dataset in $\mathbb{R}^d$, we define regions of high density that we call cluster cores. We then consider a random walk on a neighborhood graph built on top of our data points which is designed to be attracted by hig…

Paper introduces novel distances for clustering ordinal time series.

In this paper we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and learning from data streams. We combine the TWSVM with a fuzzy membership function, so that each input has a different contribution to each…

FUJI scores similarity of ranked lists more robustly.

Unified framework for hierarchical image classification with epistemic uncertainty.

New method detects concept drift in data streams with missing values.

FSN model improves few-shot learning by generalizing to new tasks.

The hybrid clustering-classification neural network is proposed. This network allows increasing a quality of information processing under the condition of overlapping classes due to the rational choice of a learning rate parameter and introducing a special procedure of fuzzy reasoning in the clustering process, which o…

Paper simulates LR fuzzy intervals with interval-valued cores.

In this paper, we have tried to apply the concepts of fuzzy sets to Lie groups and its relative concepts. First, we define a ${\cal C}^1$ fuzzy submanifold after reviewing ${\cal C}^1-$fuzzy manifold definition. In main section, we defined the Lie group and some its relative concepts such as fuzzy transformation group,…

Fuzzy eIX method evolves classifiers for online data streams.

Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.

Enhanced fuzzy system predicts chaotic time series with improved accuracy.

A new adaptive binarization technique using fuzzy integrals improves image quality.

Paper investigates differentiable fuzzy implications and their suitability for learning.

A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multi-layer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the input spaces of the layers. To design the DCFS based on input-outpu…

SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.

A new fuzzy clustering method using hyperbolic smoothing for large datasets.

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…

Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections b…

Unified probabilistic foundation for fuzzy simplicial sets in dimensionality reduction.

Proposes new random models for fuzzy clustering similarity measures.

Optimized fuzzy entropy framework improves feature selection and classification performance.

Measuring the similarity of two files is an important task in malware analysis, with fuzzy hash functions being a popular approach. Traditional fuzzy hash functions are data agnostic: they do not learn from a particular dataset how to determine similarity; their behavior is fixed across all datasets. In this paper, we …

Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize its parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensiona…

New validity index for fuzzy-possibilistic c-means clustering.

Improved stock index analysis using fuzzy parameters and machine learning.

Introduces fuzzy layers to enhance deep learning performance.

In regression problems, the use of TSK fuzzy systems is widely extended due to the precision of the obtained models. Moreover, the use of simple linear TSK models is a good choice in many real problems due to the easy understanding of the relationship between the output and input variables. In this paper we present FRU…

General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comp…

Improved TSK fuzzy regression models with MBGD-RDA and rule pruning.