arXiv research

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

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…

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…

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

A new model for detecting overlapping communities in weighted networks.

Robust fuzzy clustering for EEG driver alertness with outlier detection.

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

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…

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…

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

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…

Paper extends Bayes Theorem for interval probability estimates.

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…

A novel fuzzy clustering method for multivariate time series.

Paper introduces novel distances for clustering ordinal time series.

FUJI scores similarity of ranked lists more robustly.

FCM clustering adapts to persistence diagrams for topological data analysis.

FCPCA fuzzy clusters high-dimensional time series data efficiently.

Unified framework for hierarchical image classification with epistemic uncertainty.

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

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…

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…

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

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 …

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

Optimal fuzzy classification aggregation functions are weighted means.

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…

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…

Optimized fuzzy entropy framework improves feature selection and classification performance.

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

In this paper, a similarity-driven cluster merging method is proposed for unsuper-vised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-…

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

Paper investigates differentiable fuzzy implications and their suitability for learning.

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fa…

Unified probabilistic foundation for fuzzy simplicial sets in dimensionality reduction.

Develops a new fuzzy model using QPs and ewl2 regularization to improve local region behavior.

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.

The \emph{Mixed-Membership Stochastic Blockmodel (MMSB)} is a popular framework for modeling social network relationships. It can fully exploit each individual node's participation (or membership) in a social structure. Despite its powerful representations, this model makes an assumption that the distributions of relat…

Unified framework evaluates different nearest neighbor classification methods.

A fuzzy expert system selects stocks for BSE using AI techniques.

Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Among various clustering approaches, the family of K-Means algorithms gains popularity due to simplicity and efficiency. However, most of existing K-Means based clustering algorithms cannot deal with outliers well…

The expected utility operators introduced in a previous paper, offer a framework for a general risk aversion theory, in which risk is modelled by a fuzzy number $A$. In this paper we formulate a coinsurance problem in the possibilistic setting defined by an expected utility operator $T$. Some properties of the optimal …

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

Enhanced fuzzy system predicts chaotic time series with improved accuracy.

Proposes a fuzzy rule-based method for data visualization.