A Comparative Study on Various Binary Classification Algorithms and their Improved Variant for Optimal Performance

Bahel, Vedant; Pillai, Sofia K.; Malhotra, Manit

doi:10.1109/tensymp50017.2020.9230877

Cited by 30 publications

(13 citation statements)

References 6 publications

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“…The only dependent variable that we used was whether the tree was uprooted or not. Consequently, we came up with a binary classification problem (Bahel et al 2020).…”

Section: Intelligent Data Analysismentioning

confidence: 99%

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Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model

Dimou¹,

Demertzis²,

Kantartzis³

2022

Preprint

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Management approaches inspired by the variability of natural disturbances are expected to produce forests in the future that will be significantly more resilient and better adapted to local environmental conditions. Due to climate change, windstorms are becoming increasingly common resulting in the destruction not only of extensive forest areas but, quite often, of small-sized and scattered forest lands that can ultimately become home to insects and disease dissemination sites. In the present study, an attempt is made to identify and record areas in the northeastern forests of Greece covered by mixed stands of conifers and broadleaves that experienced massive windthrow following local storms. Based on tree-level data, local topographic features, forest characteristics and the mechanical properties of green wood, a reliable model, to be used for the prediction of similar disturbances in the future, has been created after a thorough comparative study of the most well-known intelligent machine learning algorithms.

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“…The only dependent variable that we used was whether the tree was uprooted or not. Consequently, we came up with a binary classification problem (Bahel et al 2020).…”

Section: Intelligent Data Analysismentioning

confidence: 99%

“…In a period of fifty years the average annual damage to wood from storms in Europe amounted to 18.7 million m 3 , with most windthrow damage taking place in Central Europe and the Alps. For the same period, short-term wood damage from insect attacks amounted to 2.9 million m 3 per year (Schelhaas et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model

Dimou¹,

Demertzis²,

Kantartzis³

2022

Preprint

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show abstract

“…Binary classification is a supervised learning technique, which refers to a process of mapping each given instance to a label of one of two classes using a machine learning algorithm (binary classification model). A wide variety of binary classification models have been proposed and compared in the literature [55,56]. The performance of models depends on the dataset and the classification problem.…”

Section: Existing Binary Classification Algorithmsmentioning

confidence: 99%

“…A regularization penalty on the model coefficients can also be applied with logistic regression, and is controlled with the parameter C. With large values of C, logistic regression tries to fit the training data where as for small values of C, the method tries harder to find coefficients that are closer to 0, even if that model fits the training data a little bit worse. Some advantages and disadvantages of LR model are [55,56]:…”

Section: Existing Binary Classification Algorithmsmentioning

confidence: 99%

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Sparse Recovery, Classification, and Data Compression via Improved Maximum Feasible Subsystem Algorithms

Firouzeh¹

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Two new strategies are developed in this thesis to increase the speed of the state-ofthe-art Maximum Feasible Subsystem (MAX FS) algorithms. The newly developed strategies can be combined with any MAX FS algorithm to increase its speed while preserving or improving solution quality. The improved algorithms apply in the case of dense constraint matrices such as those found when various data compression/dimensionality reduction, classification, and sparse recovery problems are converted to MAX FS problems. This approach is used for sparse recovery in Compressive Sensing (CS) and data compression via Nonnegative Matrix Factorization (NNMF) for the first time in this thesis. In CS, the new algorithm can successfully recover real-world signals such as speech and Electrocardiogram (ECG) signals that have been more greatly compressed than existing methods can handle, and with greater recovered signal quality. They also deliver sparser solutions when compared with those obtained using traditional sparse recovery algorithms. The new algorithm significantly reduces the solution time of the existing MAX FS methods. It reduces the solution time of Method B by 80.64% and 77.82% for recovery of compressively sensed ECG and speech signals for instance, respectively. Three novel MAX FS-based methods for NNMF are developed and investigated using synthetic data and real-world image datasets. The new NNMF algorithms, unlike the majority of existing NNMF methods, do not require an initialization method or prior knowledge of the matrix rank. iii These new NNMF solutions methods are also more robust to outliers and provide higher quality solution compared to the state-of-the-art NNMF algorithms. To illustrate, the new NNMF algorithm improves the relative approximation quality of the existing column subset selection NNMF algorithm by 5.2% for the ORL facial image dataset. A faster MAX FS-based algorithm for binary classification is also proposed, which improves the processing time of the existing MAX FS algorithm (Algorithm 2) by 94.77% on average. In comparison, the developed algorithm outperforms widely used binary classification algorithms by providing better results for recall-oriented machine learning tasks such as disease diagnosis. For instance, the new algorithm improves the average recall score of Support Vector Machine (SVM) by 18.16% for binary classification task.

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Evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances

Shah

Joshi

2022

Communications in Computer and Information Science

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A Comparative Study on Various Binary Classification Algorithms and their Improved Variant for Optimal Performance

Cited by 30 publications

References 6 publications

Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model

Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model

Sparse Recovery, Classification, and Data Compression via Improved Maximum Feasible Subsystem Algorithms

Evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances

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