Multithreshold Entropy Linear Classifier: Theory and applications

Czarnecki, Wojciech Marian; Tabor, Jacek

doi:10.1016/j.eswa.2015.03.007

Cited by 28 publications

(23 citation statements)

References 29 publications

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“…SVM aims to find a hyperplane that minimizes the structural risk ( Czarnecki and Tabor, 2015 ) in kernel space. Gaussian radial basis function, Linear, and polynomial are several common kernel functions.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

et al. 2020

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Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

et al. 2020

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“…In the future, we plan to consider a more general family of entropy functions, including Rényi and Tsallis entropies, which are of great importance in the theory of coding and related problems [6,20,21]. Moreover, there also arises a natural question concerning the compression of n-tuple random variables.…”

Section: Discussionmentioning

confidence: 99%

Entropy Approximation in Lossy Source Coding Problem

Śmieja

Tabor

2015

Entropy

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In this paper, we investigate a lossy source coding problem, where an upper limit on the permitted distortion is defined for every dataset element. It can be seen as an alternative approach to rate distortion theory where a bound on the allowed average error is specified. In order to find the entropy, which gives a statistical length of source code compatible with a fixed distortion bound, a corresponding optimization problem has to be solved. First, we show how to simplify this general optimization by reducing the number of coding partitions, which are irrelevant for the entropy calculation. In our main result, we present a fast and feasible for implementation greedy algorithm, which allows one to approximate the entropy within an additive error term of log 2 e. The proof is based on the minimum entropy set cover problem, for which a similar bound was obtained.

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“…where Σ A = (h γ A ) 2 cov A and (for γ being a scaling hyperparameter [6]) h γ A = γ( 4 k+2 ) 1/(k+4) |A| −1/(k+4) . Now we need the formula for A B , which is calculated [6] with the use of…”

Section: Closed Form Solution For Objective and Its Gradientmentioning

confidence: 99%

Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation

Czarnecki

Józefowicz

Tabor

2015

Machine Learning and Knowledge Discovery in Databases

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Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods. In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis.In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional linear data projection maximizing discriminativeness of projected classes. As a result we obtain a linear embedding which can be used for classification, class aware dimensionality reduction and data visualization. MELM provides highly discriminative 2D projections of the data which can be used as a method for constructing robust classifiers.We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.

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Multithreshold Entropy Linear Classifier: Theory and applications

Cited by 28 publications

References 29 publications

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Entropy Approximation in Lossy Source Coding Problem

Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation

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