Sparse solution of least-squares twin multi-class support vector machine using ℓ0 and ℓp-norm for classification and feature selection.

In the realm of multi-class classification, the twin K-class support vector classification (Twin-KSVC) generates ternary outputs {-1,0,+1} by evaluating all training data in a "1-versus-1-versus-rest" structure. Recently, inspired by the least-squares version of Twin-KSVC and Twin-KSVC, a new multi-class classifier called improvements on least-squares twin multi-class classification support vector machine (ILSTKSVC) has been proposed. In this method, the concept of structural risk minimization is achieved by incorporating a regularization term in addition to the minimization of empirical risk. Twin-KSVC and its improvements have an influence on classification accuracy. Another aspect influencing classification accuracy is feature selection, which is a critical stage in machine learning, especially when working with high-dimensional datasets. However, most prior studies have not addressed this crucial aspect. In this study, motivated by ILSTKSVC and the cardinality-constrained optimization problem, we propose ℓp-norm least-squares twin multi-class support vector machine (PLSTKSVC) with 0

Inverse free reduced universum twin support vector machine for imbalanced data classification.

Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other classes, even though the other classes might be more important. The standard classification algorithms may classify all the data into the majority class, and this is a significant drawback of most standard learning algorithms, so imbalanced datasets need to be handled carefully. One of the traditional algorithms, twin support vector machines (TSVM), performed well on balanced data classification but poorly on imbalanced datasets classification. In order to improve the TSVM algorithm's classification ability for imbalanced datasets, recently, driven by the universum twin support vector machine (UTSVM), a reduced universum twin support vector machine for class imbalance learning (RUTSVM) was proposed. The dual problem and finding classifiers involve matrix inverse computation, which is one of RUTSVM's key drawbacks. In this paper, we improve the RUTSVM and propose an improved reduced universum twin support vector machine for class imbalance learning (IRUTSVM). We offer alternative Lagrangian functions to tackle the primal problems of RUTSVM in the suggested IRUTSVM approach by inserting one of the terms in the objective function into the constraints. As a result, we obtain new dual formulation for each optimization problem so that we need not compute inverse matrices neither in the training process nor in finding the classifiers. Moreover, the smaller size of the rectangular kernel matrices is used to reduce the computational time. Extensive testing is carried out on a variety of synthetic and real-world imbalanced datasets, and the findings show that the IRUTSVM algorithm outperforms the TSVM, UTSVM, and RUTSVM algorithms in terms of generalization performance.

Effects of physical parameters on fish migration between a reservoir and its tributaries.

Reservoirs interrupt natural riverine continuity, reduce the overall diversity of the environment, and enhance the spread of non-native fish species through suitable environments. Under favourable conditions, invasive species migrate to tributaries to benefit from local resource supplies. However, the changes in physical conditions in reservoirs that motivate fish species to migrate remain poorly understood. We analysed migration between a reservoir and its tributary in three non-native (asp Leuciscus aspius, ide Leuciscus idus, and bream Abramis brama) and two native (chub Squalius cephalus and pike Esox lucius) species equipped with radio tags. This 5-year study revealed that an increasing day length was the most general predictor of migration into the tributary in all observed species except E. lucius. Only L. aspius responded to the substantially increasing water level in the reservoir, while the migration of L. idus and S. cephalus was attenuated. Abramis brama and S. cephalus occurred more frequently in tributaries with an increase in temperature in the reservoir and vice versa, but if the difference in temperature between the reservoir and its tributary was small, then A. brama did not migrate. Our results showed that migration from the reservoir mainly followed the alterations of daylight, while responses to other parameters were species specific. The interindividual heterogeneity within the species was significant and was not caused by differences in length or sex. Our results contribute to the knowledge of how reservoirs can affect the spread of non-native species that adapt to rapid human-induced environmental changes.

Novel approach to computerized breath detection in lung function diagnostics.

BACKGROUND: Breath detection, i.e. its precise delineation in time is a crucial step in lung function data analysis as obtaining any clinically relevant index is based on the proper localization of breath ends. Current threshold or smoothing algorithms suffer from severe inaccuracy in cases of suboptimal data quality. Especially in infants, the precise analysis is of utmost importance. The key objective of our work is to design an algorithm for accurate breath detection in severely distorted data. METHODS: Flow and gas concentration data from multiple breath washout test were the input information. Based on universal physiological characteristics of the respiratory tract we designed an algorithm for breath detection. Its accuracy was tested on severely distorted data from 19 patients with different types of breathing disorders. Its performance was compared to the performance of currently used algorithms and to the breath counts estimated by human experts. RESULTS: The novel algorithm outperformed the threshold algorithms with respect to their accuracy and had similar performance to human experts. It proved to be a highly robust and efficient approach in severely distorted data. This was demonstrated on patients with different pulmonary disorders. CONCLUSION: Our newly proposed algorithm is highly robust and universal. It works accurately even on severely distorted data, where the other tested algorithms failed. It does not require any pre-set thresholds or other patient-specific inputs. Consequently, it may be used with a broad spectrum of patients. It has the potential to replace current approaches to the breath detection in pulmonary function diagnostics.

Tolerance approach to possibilistic nonlinear regression with interval data.

We study possibilistic nonlinear regression models with crisp and/or interval data. Herein, the task is to compute tight interval regression parameters such that all observed output data (either crisp or interval) are covered by the range of the nonlinear interval regression function. We propose a method for determination of interval regression parameters based on the tolerance approach developed by the authors for the linear case. We define two classes of nonlinear regression models for which efficient algorithms exist. For other models, we provide some extensions allowing to calculate lower and upper bounds on the widths of the optimal interval regression parameters. We also discuss other approaches to interval regression than the possibilistic one. We illustrate the theory by examples.