Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System
The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However, since edRVFL generates features for its hidden layers through random projection, it can potentially lose intricate features or fail to capture certain non-linear features in its base models (hidden layers). To enhance the feature learning capabilities of edRVFL, we propose a novel edRVFL based on fuzzy inference system (edRVFL-FIS). The proposed edRVFL-FIS leverages the capabilities of two emerging domains, namely deep learning and ensemble approaches, with the intrinsic IF-THEN properties of fuzzy inference system (FIS) and produces rich feature representation to train the ensemble model. Each base model of the proposed edRVFL-FIS encompasses two key feature augmentation components: a) unsupervised fuzzy layer features and b) supervised defuzzified features. The edRVFL-FIS model incorporates diverse clustering methods (R-means, K-means, Fuzzy C-means) to establish fuzzy layer rules, resulting in three model variations (edRVFL-FIS-R, edRVFL-FIS-K, edRVFL-FIS-C) with distinct fuzzified features and defuzzified features. Within the framework of edRVFL-FIS, each base model utilizes the original, hidden layer and defuzzified features to make predictions. Experimental results, statistical tests, discussions and analyses conducted across UCI and NDC datasets consistently demonstrate the superior performance of all variations of the proposed edRVFL-FIS model over baseline models. The source codes of the proposed models are available at https://github.com/mtanveer1/edRVFL-FIS.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing mult…
Graph EmbeddingWeighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification
In this paper, we first introduce batch normalization to the edRVFL network. This re-normalization method can help the network avoid divergence of the hidden features. Then we propose novel variants of Ensemble Deep Rand…
DiversityNeuro-Fuzzy Random Vector Functional Link Neural Network for Classification and Regression Problems
The random vector functional link (RVFL) neural network has shown the potential to overcome traditional artificial neural networks’ limitations, such as substantial time consumption and the emergence of suboptimal soluti…
ClusteringDecision MakingNavigateEnhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces
Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used base classifiers in an ensemble framework. …
General ClassificationMulti-class ClassificationRandom Vector Functional Link Neural Network based Ensemble Deep Learning
In this paper, we propose a deep learning framework based on randomized neural network. In particular, inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) wi…
Deep LearningEnsemble Learning