scholarly journals Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Computation ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 6
Author(s):  
Muhammad Anwar Ma’sum ◽  
Hadaiq Rolis Sanabila ◽  
Petrus Mursanto ◽  
Wisnu Jatmiko
Keyword(s):  
Neural Network ◽  
Incremental Learning ◽  
Fuzzy Neural Network ◽  
Data Classification ◽  
Benchmark Dataset ◽  
Learning Approaches ◽  
Clustering Methods ◽  
Modal Data ◽  
Fuzzy Neural ◽  
Benchmark Datasets

One of the challenges in machine learning is a classification in multi-modal data. The problem needs a customized method as the data has a feature that spreads in several areas. This study proposed a multi-codebook fuzzy neural network classifiers using clustering and incremental learning approaches to deal with multi-modal data classification. The clustering methods used are K-Means and GMM clustering. Experiment result, on a synthetic dataset, the proposed method achieved the highest performance with 84.76% accuracy. Whereas on the benchmark dataset, the proposed method has the highest performance with 79.94% accuracy. The proposed method has 24.9% and 4.7% improvements in synthetic and benchmark datasets respectively compared to the original version. The proposed classifier has better accuracy compared to a popular neural network with 10% and 4.7% margin in synthetic and benchmark dataset respectively.

scholarly journals Intelligent Clustering and Dynamic Incremental Learning to Generate Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 679
Author(s):  
Muhammad Anwar Ma’sum
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Data Classification ◽  
Fuzzy Neural Networks ◽  
Benchmark Dataset ◽  
Synthetic Dataset ◽  
Special Treatment ◽  
Modal Data ◽  
Fuzzy Neural ◽  
Benchmark Datasets

Classification in multi-modal data is one of the challenges in the machine learning field. The multi-modal data need special treatment as its features are distributed in several areas. This study proposes multi-codebook fuzzy neural networks by using intelligent clustering and dynamic incremental learning for multi-modal data classification. In this study, we utilized intelligent K-means clustering based on anomalous patterns and intelligent K-means clustering based on histogram information. In this study, clustering is used to generate codebook candidates before the training process, while incremental learning is utilized when the condition to generate a new codebook is sufficient. The condition to generate a new codebook in incremental learning is based on the similarity of the winner class and other classes. The proposed method was evaluated in synthetic and benchmark datasets. The experiment results showed that the proposed multi-codebook fuzzy neural networks that use dynamic incremental learning have significant improvements compared to the original fuzzy neural networks. The improvements were 15.65%, 5.31% and 11.42% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively, for incremental version 1. The incremental learning version 2 improved by 21.08% 4.63%, and 14.35% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively. The multi-codebook fuzzy neural networks that use intelligent clustering also had significant improvements compared to the original fuzzy neural networks, achieving 23.90%, 2.10%, and 15.02% improvements on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively.

The Application of Fuzzy Neural Network in Data Mining

2011 ◽  
Vol 179-180 ◽  
pp. 930-935
Author(s):  
Wang Lan Tian
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Fuzzy Neural Network ◽  
Data Classification ◽  
Complex Data ◽  
Spatial Evolution ◽  
Urban Construction ◽  
Fuzzy Neural

Fuzzy neural network, which can deal with complex data and prediction process that other algorithms can not accomplish, has become a focus in recent years in many fields. Data mining can extract such information and knowledge as data classification, spatial evolution and prediction and so on, and in the huge cadastral data find the implied information which is helpful for our urban construction.

scholarly journals TopFuzz4SA: An Integrated Fuzzy Neural Network with Topic-aware Auto-encoding for Sentiment Analysis

2021 ◽  
Author(s):  
Tham Vo
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Semantic Information ◽  
Contextual Information ◽  
Representation Learning ◽  
Sentiment Classification ◽  
Textual Data ◽  
Fuzzy Neural ◽  
Benchmark Datasets ◽  
Textual Representation

Abstract Recent advanced deep learning architectures, such as neural seq2seq, transformer, etc. have demonstrated remarkable improvements in multi-typed sentiment classification tasks. Even though recent transformer-based and seq2seq-based models have successfully enabled to capture rich-contextual information of texts, they are still lacking of attention on incorporating the global semantic information, such as topic, in order to sufficiently leverage the performance of downstream SA task. Moreover, emotional expressions of users are normally in forms of natural human-written textual data which might consist a lot of noise and ambiguity which impose great challenges on the processes of textual representation learning as well as sentiment polarity prediction. To meet these challenges, we propose a novel integrated fuzzy-neural architecture with a topic-driven textual representation learning approach for handling SA task, called as: TopFuzz4SA. Specifically, in the proposed TopFuzz4SA model, we first apply a topic-driven neural encoder-decoder architecture with the incorporation of latent topic embedding and attention mechanism to sufficiently learn both rich contextual and global semantic information of the given textual data. Then, the achieved rich semantic representations of texts are fed into a fused deep fuzzy neural network to effectively reduce the feature ambiguity and noise, forming the final textual representations for sentiment classification task. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed TopFuzz4SA model in comparing with contemporary state-of-the-art baselines.

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