A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved stock index analysis using fuzzy parameters and machine learning.
A new fuzzy time series method for non-stationary data.
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
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…
In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach. The present paper extends this work for selecting the most appropriate rule b…
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.
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…
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 fuzzy submanifold after reviewing 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.
EGFC learns from streaming data to classify power quality disturbances.
The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy …
Efficiency criteria improve conformal predictors' performance.
New cluster validity index detects optimal number of clusters and secondary options.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
CAF-HFCM automatically forms a cluster hierarchy and optimizes the number of clusters without trial-and-validation.
Proposes a fuzzy rule-based method for data visualization.
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.
We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standar…
We review and illustrate how the volatility smile translates into a probability distribution, the market-implied probability distribution representing believes priced in. The effects of changes in the smile are examined. Special attention is given to the effects of slope, which might appear at first counter-intuitive. …
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.
In this paper, we propose a new fuzzy clustering algorithm based on the mode-seeking framework. Given a dataset in , 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…
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for -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…
Proposes new random models for fuzzy clustering similarity measures.
Optimized fuzzy entropy framework improves feature selection and classification performance.
In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the applicability of the method for regression and classification tasks using synthetic data…
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 hybrid fuzzy-crisp clustering algorithm addresses imbalanced cluster sizes.
A novel fuzzy clustering method for multivariate time series.
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…
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.
A novel weighted feature selection method using fuzzy sets improves classification accuracy and stability.
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-…
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…
Classifies central extensions for area-preserving diffeomorphisms and shows they are fuzzy sphere limits.
Optimal fuzzy classification aggregation functions are weighted means.
BFPM improves machine learning accuracy by considering object types and memberships flexibly.
Improves k-NN for monotonic data with robustness against noise.
In the study of investment problem, aside from the investment risk the background risk appears. Both the investment risk and the background risk are probabilistically described by random variables. This paper starts from the hypothesis that the two types of risk can be represented both probabilistically (by random vari…