Fuzzy min–max neural networks for categorical data: application to missing data imputation

Rey-del-Castillo, Pilar; Cardeñosa, Jesús

doi:10.1007/s00521-011-0574-x

Cited by 31 publications

(8 citation statements)

References 32 publications

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“…RNNs are shown to be very successful generative models for data completion [28] and specifically in vision [7]. Although feedforward neural networks [29,30,31], auto associative neural networks [32] and self organizing maps [33] are also used for data completion tasks, recurrent neural networks are a more natural choice due to their great generative capabilities. Restricted Boltzmann Machines (RBM) are shown to very successfully fill in the missing pixels due to visual occlusions [7], but their classification performance were not measured for standard datasets.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Classification of Occluded Objects Using Fast Recurrent Processing

Yılmaz

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For visual occlusion problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations. The proposed algorithm is tested on a widely used dataset, and shown to achieve 2× improvement in classification accuracy for occluded objects. When compared to Restricted Boltzmann Machines, our algorithm shows superior performance for occluded object classification.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Classification of Occluded Objects Using Fast Recurrent Processing

Yılmaz

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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“…Many methods have been introduced in the FMM domain [9], [10], [13], [15], [19]- [23], [27], [28], [31], and in this section we survey the most important of these methods that are similar to our proposed method, their evolution, as well as their advantages and disadvantages.…”

Section: Background and Similar Workmentioning

confidence: 99%

Multi-Level Fuzzy Min-Max Neural Network Classifier

Davtalab

Dezfoulian

Mansoorizadeh

2014

IEEE Trans. Neural Netw. Learning Syst.

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In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output of the network is formed by combining the outputs of these classifiers. MLF is capable of learning nonlinear boundaries with a single pass through the data. According to the obtained results, the MLF method, compared to the other FMM networks, has the highest performance and the lowest sensitivity to maximum size of the hyperbox parameter (θ), with a training accuracy of 100% in most cases.

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“…Missing histological data can be identified by imputation strategies based on a heuristic expectation maximization (EM) algorithm 13 . Another approach is to predict missing values using only nonmissing samples by trained classifiers such as decision trees and fuzzy neural networks 14,15 . However, these methods cannot be directly used to handle missing instance imputation 12 .…”

Section: Introductionmentioning

confidence: 99%

A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE‐MRI radiomics

Fan

You

et al. 2021

Medical Physics

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Purpose Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki‐67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics. Methods To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical, and texture were extracted from dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)‐based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation. Results By integrating radiomics from DCE‐MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC = 0.793), which was better than the NMF (AUC = 0.756) and MCF methods (AUC = 0.706; corrected p = 0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates. Conclusions DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging‐scale patterns for interactions with molecular‐scale histological information and is promising in the tumor characterization and management of patients.

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Fuzzy min–max neural networks for categorical data: application to missing data imputation

Cited by 31 publications

References 32 publications

Classification of Occluded Objects Using Fast Recurrent Processing

Classification of Occluded Objects Using Fast Recurrent Processing

Multi-Level Fuzzy Min-Max Neural Network Classifier

A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE‐MRI radiomics

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