Ensemble-based learning is a successful approach for robust partitioning. Since the ensemble classifiers cover each other fault, classification is a critical task. Clustering ensemble based learning can also be done using fusion of some primary partitions which derive from naturally different sources. In this study, a novel clustering ensemble learning method inspired from the ant colony clustering algorithm is proposed. Since ensemble methods necessarily rely on diversity, swarm intelligence algorithms, such as ant colony, are can be good options to be applied. Executing this algorithm for several times on a dataset, result in various partitions. Then, a simple partitioning algorithm is exercised to aggregate them into a consensus partitioning. The proposed clustering approach lets the parameters be free to be manipulated, and thanks to the ensemble, non-optimality of the parameters is covered. Experimental results on several real datasets illustrate the efficiency of the proposed method to generate the final partitioning.
Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.
In the real world, we face some complex and important problems that should be optimized, most of the real-world problems are dynamic.Solving dynamic optimization problems are very difficult due to possible changes in the location of the optimal solution.In dynamic environments, we are faced challenges when the environment changes.To respond to these changes in the environment, any change can be considered as the input of a new optimization problem that should be solved from the beginning, which is not suitable because it is time consuming.One technique for improving optimization and learning in dynamic environments is by using information from the past.By using solutions from previous environments, it is often easier to find promising solutions in a new environment.A common way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined at the time that the environment changes.Memory can help search respond quickly and efficiently to change in a dynamic problem.Given that a memory has a سال 1400 شمارة 3 پیاپی 49 128 finite size, if one wishes to store new information in the memory, one of the existing entries must be discarded.The mechanism used to decide whether the candidate entry should be included in the memory or not, and if so, which of the old entries should be replaced it, is called the replacement strategy.This paper explores ways to improve memory for optimization and learning in dynamic environments.In this paper, a memory with clustering and new replacement strategy for storing and restoring memory solutions has been used to enhance memory performance.The evolutionary algorithms that have been presented so far have the problem of rebuilding populations when multiple populations converge to an optimum.For this reason, we proposed algorithm with exclution mechanism that have the ability to explore the environment (Exploration) and extraction (Explitation).Thus, an optimization algorithm is required to solve the problems in dynamic environments well.In this paper, a novel collective optimization algorithm, namely the Clustering and Memory-based Parent-Child Swarm Algorithm (CMPCS), is presented.This method relies on both individual and group behavior.The proposed CMPCS method has been tested on the moving peaks benchmark (MPB).The MPB is a good Benchmark to evaluate the efficiency of the optimization algorithms in dynamic environments.The experimental results on the MPB reveal the appropriate efficiency of the proposed CMPCS method compared to the other state-of-the-art methods in solving the dynamic optimization problems.
To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable.During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes.Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm3. Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%.As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and "learns" the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that affected all postoperative outcome variables and they received the highest relative weight by the ANN system.
Conventional clustering ensemble algorithms employ a set of primary results; each result includes a set of clusters which are emerged from data. Given a large number of available clusters, one is faced with the following questions: (a) can we obtain the same quality of results with a smaller number of clusters instead of full ensemble? (b) If so, which subset of clusters is more efficient to be used in the ensemble? In this paper, these two questions are going to be answered. We explore a clustering ensemble approach combined with a cluster stability criterion as well as a dataset simplicity criterion to discover the finest subset of base clusters for each kind of datasets. Also, a novel method is proposed in order to accumulate the selected clusters and to extract final partitioning. Although it is expected that by reducing the size of ensemble the performance decreases, our experimental results show that our selecting mechanism generally lead to superior results.
Bagging and Boosting are two main ensemble approaches consolidating the decisions of several hypotheses. The diversity of the ensemble members is considered to be a significant element to obtain generalization error. Here, an inventive method called EBAGTS (ensemble-based artificially generated training samples) is proposed to generate ensembles. It manipulates training examples in three ways in order to build various hypotheses straightforwardly: drawing a sub-sample from training set, reducing/raising error-prone training instances, and reducing/raising local instances around error-prone regions. The proposed method is a straightforward, generic framework utilizing any base classifier as its ensemble members to assemble a powerfully built combinational classifier. Decision-tree classifier and multilayer perceptron classifier as some basic classifiers have been employed in the experiments to indicate the proposed method accomplish higher predictive accuracy compared to meta-learning algorithms like Boosting and Bagging. Furthermore, EBAGTS outperforms Boosting more impressively as the training data set gets broader. It is illustrated that EBAGTS can fulfill better performance comparing to the state of the art.
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