A supervised growing neural gas algorithm for cluster analysis

Jirayusakul, A.; Auwatanamongkol, Surapong

doi:10.3233/his-2007-4205

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…The function was created to encourage obtaining the cluster representative as being pure by having fewer samples from other classes within the neighbor and as few clusters as possible. Using the impurity idea, [22] proposed supervised growing neural gas (SGNG) to take class information into account and [23] updated SCEC to include additional objective fitness functions. To the best of our investigation, although the proposed impurity idea performed well on the supervised clustering task, it is not an ideal solution to class decomposition for training a classifier.…”

Section: Introductionmentioning

confidence: 99%

A new density core graph-cut class decomposition to improve neural network classification performance

Tantamjarik¹,

Tanprasert²

2022

IJEECS

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<span>This research presents a new pre-processed class decomposition technique called density core graph-cut (DCGC). The method uses supervised clustering instead of a traditional unsupervised one to decompose the class. Supervised clustering requires additional label information to function and with that it gains a better understanding of the distribution. DCGC employs nearest neighbors to form a density core graph for each class. Then, the edges of each graph to be removed or cut is identified utilizing class information. Lastly, it yields final clusters by grouping all connected cores and assigning the remaining samples to a cluster where the nearest core belongs. Training neural network classifiers on complex label data will yield a better accuracy with the modified class representation. Intuitively, the decision boundaries separating classes based on the modified labels are less complex, and classifiers’ chance to reach these hyperplanes is higher. The results present that training neural networks using label representations from DCGC significantly helps improve the classification accuracy of neural networks on syntactic datasets as high as 30%. For real-world problems, the experiment presents a mixed result in which some datasets moderately benefit from DCGC.</span>

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Section: Introductionmentioning

confidence: 99%

A new density core graph-cut class decomposition to improve neural network classification performance

Tantamjarik¹,

Tanprasert²

2022

IJEECS

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Neural Gas Based Classification of Globular Clusters

Angora

Brescia

Cavuoti

et al. 2018

Communications in Computer and Information Science

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0000−0002−0316−6562] , Massimo Brescia 2[0000−0001−9506−5680] , Stefano Cavuoti 1,2,3[0000−0002−3787−4196] , Giuseppe Riccio 2[0000−0001−7020−1172] , Maurizio Paolillo 1[0000−0003−4210−7693] , and Thomas H. Puzia 4[0000−0003−0350−7061]Abstract. Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multidimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.

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