Articles
International Journal of Environmental Science and Technology (17351472)22(5)pp. 3051-3062
Effective utilization of data analysis techniques is paramount in addressing the complex challenges presented by environmental issues. These methodologies empower researchers and practitioners to derive meaningful insights from intricate datasets encompassing air quality, biodiversity, climate change, and other pivotal environmental factors. Through the deployment of robust classification models, such as intelligent classifiers, researchers can accurately classify and predict environmental phenomena. This capability holds significant implications for guiding policy decisions, mitigating environmental risks, and devising sustainable solutions to protect our natural resources and ecosystems. Thus, classification models not only deepen our comprehension of environmental dynamics but also empower proactive measures towards achieving environmental sustainability and resilience amidst global challenges. Intelligent classifiers, distinguished by their exceptional capabilities, have demonstrated superior performance compared to other classification models. However, in all developed intelligent classifiers a similar cost/loss function is implemented in the learning processes, which is continuous and works based on the distance between actual and fitted values. Whereas the nature of the classification is discrete. As a result, in this study, a novel cost/loss function is proposed that in contrast to its conventional version is discrete and works based on the direction. In order to explain the process of the suggested methodology, the feed-forward multilayer perceptrons that are among the most famous intelligent classifiers is considered. In this paper, in order to determine the superiority of the proposed model in the domain of environment, it is implemented on some benchmark data sets which is related to air quality. Numerical results indicate that the performance of the proposed model is better than the conventional multilayer perceptrons in whole benchmark data sets. In addition, numerical results clarify that the developed discrete learning-based multilayer perceptron classifier can averagely gain an 87.68% classification rate, which points to more than 9% improvement over its conventional version. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2024.
Multimedia Tools and Applications (13807501)83(32)pp. 78269-78292
Precise diagnosis of benign and malignant breast cancer plays an important role in the effective treatment of breast cancer patients. Several classification models with different characteristics have been developed and used in a wide range of breast cancer domains to improve classification accuracy. Although the classification models differ in different aspects, they all have the same logic in their learning processes and use a continuous distance-based cost function. However, using a continuous distance-based function as a cost function in the learning processes of the traditional classification models is unreasonable or at least insufficient; since the goal function of the classification, is discrete. Hence, developing a discrete cost function for learning the classification problems, due to more consistency, may improve the classification rate; but, it has been neglected in the literature. In this paper, in contrast to all traditional continuous distance-based learning processes, a novel discrete learning-based process is proposed and implemented on a multilayer perceptron to yield a more consistent intelligent classifier. Then, the proposed discrete learning-based multilayer perceptron (DIMLP) is used for breast cancer classification. Empirical results of the breast cancer datasets indicate that the proposed DIMLP model can averagely achieve the classification rate of 94.70%, while the classification rate for the traditional MLP model is only equal to 88.54%. Therefore, the proposed DIMLP can be an appropriate and efficient alternative model for intelligent breast cancer classification, especially when more accurate results and/or a more reasonable model are required. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Applied Soft Computing (15684946)161
Hybridization of classifiers can often yield outperformed performance compared to its best individual component and mostly have more generalization ability. The majority of combined classifiers reported in the literature benefit the parallel or ensemble topology. The performance of such parallel hybrid classifiers significantly depends on their applied weighting approaches and the accuracy strongly relies on the classifiers' weights. In the literature, several different weighting mechanisms have been developed to yield a higher classification rate, which can be generally categorized into three main categories individual classifiers, averaging-based, and optimization-based algorithms. Although these weighting mechanisms have been commonly and frequently used for parallel hybridization, none of them can guarantee that their obtained classification rate will be optimum. In addition, due to the use of iterative procedures, especially by meta-heuristic optimization-based algorithms, their computational time and cost are always unsatisfactory. In this paper, a linear direct optimal weighting (LDOW) approach is proposed in which it can be generally guaranteed that the optimum classification rate will be directly reached. In this way, the proposed approach can achieve the highest classification rate by the desired computational time and cost and both mentioned limitations of currently-used weighting algorithms can be simultaneously lifted. Empirical results of twenty-three different benchmark data sets from six dissimilar domains indicate that the proposed LDOW approach can yield more classification rate in all data sets and can averagely improve 5.36% the performance of its traditional linear direct non-optimal version with the same components. Furthermore, the proposed LDOW approach can averagely improve 6.87% the classification rate of the averaging weighting algorithms that are similar to the proposed algorithm are direct ones. In addition, the proposed model can even improve 2.80% the classification rate of nonlinear intelligent weighting algorithms, on average. Whereas, the proposed approach is a linear model and its complexity and computational cost are significantly lower than these nonlinear intelligent algorithms. Numerical results also indicate that the proposed model can averagely improve 6.08% the classification rate of meta-heuristic-based weighting algorithms. While the proposed model is a direct model and its computational cost is meaningfully lower than these iterative algorithms. Thus, in theory as well as in practice, it can be inferred that the proposed LDOW approach can be an efficient alternative weighting method for parallel hybridization in the classification field. This is particularly relevant when more accurate results are required or for big data situations where computational time and cost are critical factors to consider. © 2024 Elsevier B.V.
Wireless Personal Communications (1572834X)134(2)pp. 1075-1092
Credit scoring or predicting bankruptcy is among the most crucial techniques for identifying high-risk and low-risk credit situations. Accordingly, enhancing the accuracy of bankruptcy prediction methods decreases the risk of inappropriate financial decisions. Also, increasing the accuracy of credit scoring models brings significant benefits such as improved turnover, credit market growth, proper and efficient allocation of financial resources, and sustained improvement of the profits of banks, investors, funds, and governments. Various statistical classification methods have been developed in the literature with different features and characteristics for more accurate bankruptcy prediction. However, despite all appearance differences in statistical classification approaches, they all adhere to a common idea and concept in their training procedures. The basic operation logic in whole-developed statistical classification methods focuses on maximizing a continuous distance-based cost function to yield the highest performance. Despite it being a common and frequently used procedure for classification purposes, it is an unreasonable and inefficient manner to achieve maximum accuracy in a discrete classification field. In this paper, a new discrete direction-based Logistic Regression that is a common statistical classifier method for bankruptcy forecasting is proposed. In the proposed Logistic Regression, in contrast to all traditionally developed statistical classifiers, the compatibility of the cost function and the training procedure is considered. While it can be shown overall that the performance of the presented discrete direction-based classifier will not be inferior to its continuous counterpart, an evaluation of the suggested classifier is conducted to ascertain its superiority. For this purpose, three credit scoring datasets are considered to assess the classification rate of the presented classifier. Empirical outcomes demonstrate that, as pre-expected, in all cases, the model put forward can attain a superior performance compared to conventional alternatives. These findings clearly demonstrated the significant influence of the consistency between the cost function and the training process on the classification capability, a consideration absent in any of the traditional statistical classification procedures. Consequently, the presented Logistic Regression can be considered an efficient alternative for credit scoring purposes to achieve more accurate results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Cognitive Computation (18669964)16(3)pp. 1345-1363
Classification is one of the most well-known data mining branches used in diverse domains and fields. In the literature, many different classification techniques, such as statistical/intelligent, linear/nonlinear, fuzzy/crisp, shallow/deep, and single/hybrid, have been developed to cover data and systems with different characteristics. Intelligent classification approaches, especially deep learning classifiers, due to their unique features to provide accurate and efficient results, have recently attracted a lot of attention. However, in the learning process of the intelligent classifiers, a continuous distance-based cost function is used to estimate the connection weights, though the goal function in classification problems is discrete and using a continuous cost function in their learning process is unreasonable and inefficient. In this paper, a novel discrete learning–based methodology is proposed to estimate the connection weights of intelligent classifiers more accurately. In the proposed learning process, they are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the connection weights are continuously adjusted and step by step near the target. In the present research, the proposed methodology is exemplarily applied to the deep neural network (DNN), which is one of the most recognized deep classification approaches, with a solid mathematical foundation and strong practical results in complex problems. Although the proposed methodology is just implemented on the DNN, it is a general methodology that can be similarly applied to other shallow and deep intelligent classification models. It can be generally demonstrated that the performance of the proposed discrete learning–based DNN (DIDNN) model, due to its consistency property, will not be worse than the conventional ones. The proposed DIDNN model is exemplarily evaluated on some well-known cancer classification benchmarks to illustrate the efficiency of the proposed model. The empirical results indicate that the proposed model outperforms the conventional versions of the selected deep approach in all data sets. Based on the performance analysis, the DIDNN model can improve the performance of the classic version by approximately 3.39%. Therefore, using this technique is an appropriate and effective alternative to conventional DNN-based models for classification purposes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
