A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering

Wikaisuksakul, Siripen

doi:10.1016/j.asoc.2014.08.036

Cited by 84 publications

(21 citation statements)

References 19 publications

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“…NT ← the total number of training instances. (d) Binary tournament selection Binary tournament selection technique [58] is utilized in this work to choose parents for creating new generation. Accordingly, two individuals are picked haphazardly for playing a tournament.…”

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confidence: 99%

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

2019

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In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.

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confidence: 99%

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

2019

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“…Whereas Figure 11 to Figure 12 gives a visual representation of the actual clusters and best-obtained clusters for artificial data AD_3_2, and AD_4_3, respectively. The results of all other algorithms that have been considered here for comparison with the proposed algorithm are referred from paper (Bandyopadhyay, 2011;Wikaisuksakul, 2014). All these scores have been averaged over 30 independent runs of AC-EMODE.…”

Section: Measures Used For Cluster Quality Evaluationmentioning

confidence: 99%

Automatic Clustering by Elitism based Multi-Objective Differential Evolution

Nayak

Rout

Jagadev

2017

IJMDM

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To arrange the uncategorised and unlabelled data into different clusters and finding the actual label of each datum from the huge volume by extracting useful and unique information is a real challenge. In this article, an automatic clustering by elitism-based multi-objective differential evolution (AC-EMODE) algorithm has been proposed to deal with partitioning the data into different clusters. This work includes three objectives to handle complex datasets. This generates a suitable Pareto front by simultaneously optimising three objectives. In addition to that, a very effective concept is followed for getting the best solution from the optimal Pareto front. A comparative analysis of the proposed approach with another six population-based methods has been carried out. These techniques are applied to ten datasets and the results reveal that the proposed approach can be considered as one of the alternative powerful methods for all data clustering applications in various fields.

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“…Fuzzy c means (FCM) [8] is a representative fuzzy clustering algorithm. Based on this approach, there are many varieties [9,10]. Nowadays, many FCM-type clustering algorithms are designed as incremental approaches to support continuous data streams, as most Web datasets are known to be large and high dimensional.…”

Section: Introductionmentioning

confidence: 99%

Information bottleneck based incremental fuzzy clustering for large biomedical data

Liu

Wei

2016

Journal of Biomedical Informatics

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Incremental fuzzy clustering combines advantages of fuzzy clustering and incremental clustering, and therefore is important in classifying large biomedical literature. Conventional algorithms, suffering from data sparsity and high-dimensionality, often fail to produce reasonable results and may even assign all the objects to a single cluster. In this paper, we propose two incremental algorithms based on information bottleneck, Single-Pass fuzzy c-means (spFCM-IB) and Online fuzzy c-means (oFCM-IB). These two algorithms modify conventional algorithms by considering different weights for each centroid and object and scoring mutual information loss to measure the distance between centroids and objects. spFCM-IB and oFCM-IB are used to group a collection of biomedical text abstracts from Medline database. Experimental results show that clustering performances of our approaches are better than such prominent counterparts as spFCM, spHFCM, oFCM and oHFCM, in terms of accuracy.

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A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering

Cited by 84 publications

References 19 publications

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

Automatic Clustering by Elitism based Multi-Objective Differential Evolution

Information bottleneck based incremental fuzzy clustering for large biomedical data

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