Sometimes, the reliability in decision of a classifier is more important than its recognition rate. Military and security applications are clear examples to show the importance of this measure. For example, the inability of an automatic targets recognition system to distinguish all types of military planes increases its error rate but the decision of this system for recognition of military targets should be accompanied with maximum reliability and never should be considered a civilian as a military target. This paper presents an ENSEMBLE classifier with high reliability by using multi-objective heuristic methods. Moreover, ENSEMBLE size and error rate have been minimized. Multi-Objective Particle Swarm Optimization Algorithm and Multi-Objective Inclined Planes Optimization Algorithm are the multi-objective heuristic methods which are used in this paper. The recent method is applied to DESIGN ENSEMBLE classifiers for the first time. Due to the ability of multi-objective heuristic methods in presentation of the Pareto front, it's possible to create various and user-defined conditions; conditions in which the importance of each factor (ENSEMBLE size, error rate and reliability) can be strengthened and weakened.

An emerging technique to improve classification performance is to build several different classifiers, and then to combine them, known as multiple classifier systems or ENSEMBLE classification systems. The DESIGN process of an ENSEMBLE system generally involves two steps: the collection of an ENSEMBLE of classifiers and the DESIGN of the combination rule. Researchers in various fields including pattern recognition, machine learning and statistics have examined the use of ENSEMBLE systems. Nabavi-Kerizi and Kabir provided a review of ENSEMBLE classification, where combining techniques have been mainly considered. However, the trend of recent papers in this active field shows that the ENSEMBLE systems have focused on different ways to DESIGN the ENSEMBLE of classifiers. In this paper, first we aim to establish a framework for different approaches. Based on this architecture, each approach has been introduced in details. Combination methods are then described in brief. At the end, active research areas in the field of ENSEMBLE learning are presented.

Intracytoplasmic sperm injection (ICSI) is one of the most successful techniques of Assisted Reproductive Technology (ART) and is mostly in use for the treatment of infertility with male factors. In this method, before injecting sperm into the intracytoplasmic of the oocyte, cumulus cells around the oocyte must be stripped to facilitate the injection process. To achieve this, both enzymatic and mechanical methods are used in embryological laboratories for denudation, which has major deficiencies, including the possibility of damaging the oocyte prior to the injection process. In this research, a microfluidic-based device is introduced for the separation of cumulus cells around the oocyte with minimum manual operations. The results prove high efficiency, and non-destructive denudation of the oocyte with the reduced amount of culture medium leads to the low-cost preparation process of oocytes. The process can also be integrated with ICSI chips under development and will be reported shortly.

In many cases, ENSEMBLE weather forecasts produced by numerical weather prediction (NWP) models exhibit systematic bias and under-dispersion. Over the past two decades, various ENSEMBLE post-processing approaches have been developed to address this issue. These approaches include classical methods such as ENSEMBLE model output statistics (EMOS), Bayesian model averaging (BMA), and advanced machine learning-based approaches.In most ENSEMBLE post-processing approaches, it is implicitly assumed that there is statistical independence between different forecast margins, such as lead time, location, and meteorological variables. However, this assumption is not valid for realistic forecast application scenarios. End users may be interested in scenarios such as total hydrological basin precipitation, temporal evolution of precipitation, or the interaction of precipitation and temperature, especially when temperatures are close to zero degrees Celsius. Important examples include hydrological applications, air traffic management, and energy forecasting. Such dependencies exist in raw ENSEMBLE forecasts, but these dependencies are ignored if standard univariate post-processing methods are applied separately to each margin.In recent years, various multivariate post-processing methods have been proposed. These methods can be categorized into two approaches. The goal of the first approach is to directly model the joint distribution by fitting a specific multivariate probability distribution. This approach is mainly used in low-dimensional problems or when a specific structure is chosen for the application at hand. For example, multivariate models for temperature across space, for wind vectors, and joint models for temperature and wind speed.The second approach is a two-step approach. In the first step, univariate post-processing methods are applied independently to all dimensions, and samples are generated from the resulting probability distributions. In the second step, the multivariate dependencies are recovered by reordering the univariate sample values according to the ranking order structure of a specific multivariate dependence pattern. Mathematically, this is equivalent to using a copula (parametric or nonparametric). Examples include ENSEMBLE copula coupling (ECC), Schaake Shuffle, and the Gaussian copula approach.This paper presents multivariate ENSEMBLE post-processing of temperature, two meter above ground using the ECC approach. The EMOS method is used for univariate post-processing. The performance of the raw ENSEMBLE, EMOS post-processed ENSEMBLE, and ECC systems is evaluated using energy score (ES) and variogram score (VS). The ECMWF 51-member ENSEMBLE system is used as raw data for the period from January 1, 2018 to December 31, 2023.The results showed that in addition to eliminating the bias of the raw ENSEMBLE forecast, the ECC method also preserved the dependence structure between the ENSEMBLE members. In contrast, the EMOS method only eliminated the biases without considering the dependence between the ENSEMBLE members. Because of its ability to preserve the dependence structure, the ECC method was able to achieve significantly better results than the EMOS method on a variety of metrics, including energy scores and variogram score. This suggests that the ECC method is a valuable tool for ENSEMBLE post-processing, and that it should be considered for a wide range of applications.

One of the crucial stages in machine learning in high-dimensional datasets is feature selection. Unrelated features weaknesses the efficiency of the model. However, merging several feature selection strategies is routine to solve this problem, the way to integrate feature selection methods is problematic. This paper presents a new ENSEMBLE of heuristics through fuzzy Type-I based on Ant Colony Optimization (ACO) for ENSEMBLE feature selection named Ant-EHFS. At first, three feature selection methods are run; then, the Euclidean Distance between each pair of features is computed as a heuristic (an M×M matrix is constructed), that M is the total of features. After that, a Type-I fuzzy is used individually to address various feature selections' uncertainty and estimate trustworthiness for each feature, as another heuristic. A complete weighted graph based on combining the two heuristics is then built; finally, ACO is applied to the complete graph for finding features that have the highest relevance together in the features space, which in each ant considers the reliability rate and Euclidean Distance of the destination node together for moving between nodes of the graph. Five and eight robust and well-known ENSEMBLE feature selection methods and primary feature selection methods, respectively, have been compared with Ant-EHFS on six high-dimensional datasets to show the proposed method's performance. The results have shown that the proposed method outperforms five ENSEMBLE feature selection methods and eight primary feature selections in Accuracy, Precision, Recall, and F1-score metrics.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to DESIGN a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the DESIGNed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing DESIGN of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.