Learning vector quantization classifiers for ROC-optimization

Villmann, Thomas; Kaden, Marika; Hermann, W.; Biehl, Michael

doi:10.1007/s00180-016-0678-y

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…This corresponds to the probability that a threshold value Θ exists, at which the classifier would separate such a pair of inputs correctly. This intuitive interpretation of the AU C in the ROC-analysis also makes it possible to perform the training of a classifier in such a way that the expected AU C is maximized, for details see [HR04,VKHB16].…”

Section: Performance Measures 181mentioning

confidence: 99%

The Shallow and the Deep: A biased introduction to neural networks and old school machine learning

Biehl

2023

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The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.

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Section: Performance Measures 181mentioning

confidence: 99%

The Shallow and the Deep: A biased introduction to neural networks and old school machine learning

Biehl

2023

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“…Tujuan utama dari LVQ adalah untuk mencapai tingkat akurasi tertinggi dalam klasifikasi. [14] Mengenai struktur bangunan, kuantisasi vektor pembelajaran dapat diperhatikan di gambar.…”

Section: Learning Vector Quantizationunclassified

Optimasi Identifikasi Adenokarsinoma Dari Citra X-Ray Dengan Metode Learning Vector Quantization

Amrullah Septian,

Mulyana Iskandar

2022

smartcomp

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Adenokarsinoma adalah jenis kanker yang berkembang dan menyebar di sejumlah organ tubuh manusia, terutama yang memiliki kelenjar dalam, antara lain payudara, paru-paru, kerongkongan, usus besar, pankreas, dan prostat. Karena adenokarsinoma dapat mempengaruhi berbagai organ tubuh dan menyebabkan gejala berkembang secara berbeda, sinar-x adalah satusatunya metode yang dapat dipercaya untuk mendiagnosis kondisi tersebut. Analisis data sinar-X akan mengungkapkan ada tidaknya adenokarsinoma. Periksa ini untuk mengklasifikasikan hasil rontgen sebagai normal atau kanker. Learning Vector Quantification (LVQ) dan ekstraksi ciri merupakan teknik klasifikasi. Upaya yang dilakukan dengan Klasifikasi citra diterapkan sebelum Grayscale, kemudian karakteristik ekstraksi digunakan. First-Rate Kualitas nilai parameter adalah mu, deviasi, skewness, energy, entropy, dan smoothness. 150 gambar x-ray akan diperiksa, dan Learning Vector Quantization akan digunakan untuk mengklasifikasikan hasilnya. Pada epoch 100, akurasi penelitian ini mencapai puncaknya, 97,3%, dan learning rate antara 0,1 dan 0,2.

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“…Fortunately, this can easily be realized using structured input consisting of pairs of data vectors corresponding to both classes. For details we refer to [94,90]. ROC curve for a classifier with continuous discriminant function and parameter γ.…”

Section: Beyond the Accuracy -Optimization Of Other Statistical Classmentioning

confidence: 99%

“…The area under the ROC-curve (AUROC) is equal to the probability P AB that a classifier will rank a randomly chosen A-instance higher than a randomly chosen B-instance. (according to [90])…”

Section: Beyond the Accuracy -Optimization Of Other Statistical Classmentioning

confidence: 99%

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Villmann

Bohnsack

Kaden

2016

Journal of Artificial Intelligence and Soft Computing Research

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Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.Thus, the intention of the paper is to provide a comprehensive overview of the stateof-the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.

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Learning vector quantization classifiers for ROC-optimization

Cited by 10 publications

References 47 publications

The Shallow and the Deep: A biased introduction to neural networks and old school machine learning

The Shallow and the Deep: A biased introduction to neural networks and old school machine learning

Optimasi Identifikasi Adenokarsinoma Dari Citra X-Ray Dengan Metode Learning Vector Quantization

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

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