FCM classifier for high-dimensional data

Ichihashi, Hidetomo; Honda, Katsuhiro; Notsu, Akira; Miyamoto, Eri

doi:10.1109/fuzzy.2008.4630366

Cited by 15 publications

(1 citation statement)

References 21 publications

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“…AdaBoost ( Ichihashi et al, 2008 ) is one of the most successful ensemble learning algorithms that iteratively selects several classifier instances by maintaining an adaptive weight distribution over the training examples. AdaBoost forms a linear combination 20 of selected classifier instances to create an overall ensemble.…”

Section: Methodsmentioning

confidence: 99%

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Zhang

et al. 2023

PeerJ Computer Science

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In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items’ scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model’s score prediction, which also proves the method’s universality.

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Section: Methodsmentioning

confidence: 99%

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Zhang

et al. 2023

PeerJ Computer Science

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Classifier of BOLD signals from active and inactive brain states using FCM clustering and evolutionary algorithms

Ichihashi

Honda

Notsu

et al. 2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. This paper reports a result of test on a data set with high-dimensional feature values. For classifying the blood oxygen level dependent (BOLD) responses of the brain, a way of directly handling highdimensional fMRI signals is applied. Our goal is to distinguish the BOLD responses to recalling tasks from those to resting (i.e., a binary classification problem). We use the signals from wide areas of the brain, which forms a set of high dimensional data vectors.The FCM classifier is compared with support vector machine (SVM). SVM is a high performance classifier and well suited for binary classification problems, since the size of the kernel matrix of SVM depends only on the number of instances. The error rate on the test set by the FCM classifier surpassed the SVM, though the SVM can easily handle sets of high dimensional feature vectors.

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Benchmarking parameterized fuzzy c-Means classifier

Ichihashi

Nagaura

Notsu

et al. 2009

2009 IEEE International Conference on Fuzzy Systems

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This paper reports on the performance of the fuzzy c-means based classifier (FCMC). Test set performances optimized by way of several CV procedures and three sets of hyperparameters are throughly compared. UCI benchmark datasets are used to evaluate the performance. FCM classifier in combination with standard 10-CV procedure or resubstitution (i.e., 1-CV) procedure for parameter selection achieves good test set performance compared to k-nearest neighbor classifier (k-NN). Randomized test sets performance of the classifier is comparable to that of the support vector machine (SVM) reported in the literature.

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FCM classifier for high-dimensional data

Cited by 15 publications

References 21 publications

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Classifier of BOLD signals from active and inactive brain states using FCM clustering and evolutionary algorithms

Benchmarking parameterized fuzzy c-Means classifier

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