A QSAR classification model of skin sensitization potential based on improving binary crow search algorithm

Classifying of skin sensitization using the quantitative structure-activityrelationship (QSAR) model is important. Applying descriptor selection isessential to improve the performance of the classification task. Recently, abinary crow search algorithm (BCSA) was proposed, which has been successfully applied to solve variable selection. In this work, a new time-varyingtransfer function is proposed to improve the exploration and exploitation capability of the BCSA in selecting the most relevant descriptors in QSAR classification model with high classification accuracy and short computing time.The results demonstrated that the proposed method is reliable and can reasonably separate the compounds according to sensitizers or non-sensitizerswith high classification accuracy

Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection

Reduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation based elastic penalty (CBEP), was used to overcome the limitation of LASSO and elastic net in variable selection when there are perfect correlation among explanatory variables. The performance of the CBEP was demonstrated through its application in analyzing two well-known high dimensional binary classification data sets. The CBEP provided superior classification performance and variable selection compared with other existing penalized methods. It is a reliable penalized method in binary logistic regression

Improved estimators in Bell regression model with application

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modeling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators.Comment: 19 pages; 1 figures and 2 table

Shrinkage estimators in zero-inflated Bell regression model with application

We propose Stein-type estimators for zero-inflated Bell regression models by incorporating information on model parameters. These estimators combine the advantages of unrestricted and restricted estimators. We derive the asymptotic distributional properties, including bias and mean squared error, for the proposed shrinkage estimators. Monte Carlo simulations demonstrate the superior performance of our shrinkage estimators across various scenarios. Furthermore, we apply the proposed estimators to analyze a real dataset, showcasing their practical utility.Comment: 16 pages, 1 figures, 2 table

Restricted ride estimator in the Inverse Gaussian regression model

The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. Its parameters are usually estimated using maximum likelihood (ML) method. However, the ML method is very sensitive to multicollinearity. Ridge estimator was proposed in inverse gaussian regression model. A restricted ridge estimator is proposed. Simulation and real data example results demonstrate that the proposed estimator is outperformed ML and inverse Gaussian ridge estimator

POPRAWA PARAMETRÓW REGRESJI WEKTORA NOŚNEGO V Z RÓWNOLEGŁYM WYBOREM CECHY POPRZEZ WYKORZYSTANIE ALGORYTMU QUASI-OPOZYCYJNEGO I ALGORYTMU OPTYMALIZACJI HARRIS HAWKS

Numerous real-world problems have been addressed using support vector regression, particularly v-support vector regression (v-SVR), but some parameters need to be manually changed. Furthermore, v-SVR does not support feature selection. Techniques inspired from nature were used to identify features and hyperparameter estimation. The quasi-oppositional Harris hawks optimization method (QOBL-HHOA) is introduced in this research to embedding the feature selection and optimize the hyper-parameter of the v-SVR at a same time. Results from experiments performed using four datasets. It has been demonstrated that, in terms of prediction, the number of features that may be chosen, and execution time, the suggested algorithm performs better than cross-validation and grid search methods. When compared to other nature-inspired algorithms, the experimental results of the QOBL-HHOA show its efficacy in improving prediction accuracy and processing time. It demonstrates QOBL-ability as well. By searching for the optimal hyper-parameter values, HHOAs can locate the features that are most helpful for prediction tasks. As a result, the QOBL-HHOA algorithm may be more appropriate than other algorithms for identifying the data link between the features of the input and the desired variable. Whereas, the numerical results showed superiority this method on these methods, for example, mean square error of QOBL-HHOA method results (2.05E-07) with influenza neuraminidase data set was the better than the others. For making predictions in other real-world situations, this is incredibly helpful.Liczne problemy występujące w świecie rzeczywistym rozwiązano za pomocą regresji wektora nośnego, w szczególności regresji wektora nośnego v (v-SVR), ale niektóre parametry wymagają ręcznej zmiany. Ponadto v-SVR nie obsługuje wyboru funkcji. Do identyfikacji cech i estymacji hiperparametrów wykorzystano techniki inspirowane naturą. W tym badaniu wprowadzono quasi-opozycyjną metodę optymalizacji Harris Hawks (QOBL-HHOA), aby osadzić selekcję cech i jednocześnie optymalizować hiperparametr v-SVR. Wyniki eksperymentów przeprowadzono przy użyciu czterech zbiorów danych. Wykazano, że pod względem predykcji, liczby możliwych do wybrania cech oraz czasu wykonania zaproponowany algorytm sprawdza się lepiej niż metody krzyżowej walidacji i wyszukiwania siatki. W porównaniu z innymi algorytmami inspirowanymi naturą wyniki eksperymentalne QOBL-HHOA pokazują jego skuteczność w poprawianiu dokładności przewidywań i czasu przetwarzania. Wykazuje również zdolność QOBL. Wyszukując optymalne wartości hiperparametrów, HHOA mogą zlokalizować funkcje, które są najbardziej przydatne w zadaniach predykcyjnych. W rezultacie algorytm QOBL-HHOA może być bardziej odpowiedni niż inne algorytmy do identyfikacji łącza danych pomiędzy cechami wejścia a pożądaną zmienną. Natomiast wyniki numeryczne wykazały wyższość tej metody nad wymienionymi metodami, na przykład błąd średniokwadratowy wyników metody QOBL-HHOA (2,05E-07) z zestawem danych dotyczących neuraminidazy grypy był lepszy niż w pozostałych. Jest to niezwykle pomocne przy przewidywaniu innych sytuacji w świecie rzeczywistym

Restricted ride estimator in the Inverse Gaussian regression model

The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. Its parameters are usually estimated using maximum likelihood (ML) method. However, the ML method is very sensitive to multicollinearity. Ridge estimator was proposed in inverse gaussian regression model. A restricted ridge estimator is proposed. Simulation and real data example results demonstrate that the proposed estimator is outperformed ML and inverse Gaussian ridge estimator

Non-transformed principal component technique on weekly construction stock market price

The fast-growing urbanization has contributed to the construction sector be- coming one of the major sectors traded in the world stock market. In general, non- stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Hence, the non-transformation technique using a generalized dynamic principal component (GDPC) were considered for this study. Comparison of GDPC was performed with two transformed principal component techniques. This is pertinent as to observe a larger perspective of both techniques. Thus, the latest weekly two-years observations of nine constructions stock market price from seven different countries were applied. The data was tested for stationarity before performing the analysis. As a re- sult, the mean squared error in the non-transformed technique shows eight lowest values. Similarly, eight construction stock market prices had the highest percentage of explained variance. In conclusion, a non-transformed technique can also present a better result outcome without the stationarity transformation

Variable selection in gamma regression model using chaotic firefly algorithm with application in chemometrics

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Regression modeling has received much attention in several science fields. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, chaotic firefly algorithm is proposed to perform variable selection for gamma regression model.  A real data application related to the chemometrics is conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods

Tuning parameter selectors for bridge penalty based on particle swarm optimization method

The bridge penalty is widely used as a penalty for selecting and shrinking predictors in regression models. Although its effectiveness is sensitive to the parameters you decide to use for shrinking and adjusting. The shrinkage and tuning parameters of the bridge penalty are chosen concurrently, and a continuous optimization process called particle swarm optimization is proposed as a means to do this. If implemented, the proposed method will greatly facilitate regression modeling with superior prediction performance. The results show that the proposed method is effective in comparison to other well-known methods, but this varies greatly depending on the simulation setup and the real data application