Ensemble learning has gained success in machine with major advantages over other methods. Bagging is a prominent ensemble method that creates subgroups of data, known as bags, are trained by individual methods such decision trees. Random forest example bagging additional features the process. Evolutionary algorithms have been for optimisation problems and also used learning. gradient-free work ...

Aiming at the problems of the traditional feature selection methods that threshold filtering loses a lot of effective architectural information and the shortcoming of Bagging algorithm that weaker classifiers of Bagging have the same weights to improve the performance of Chinese architectural document categorization, a new algorithm based on Rough set and Confidence Attribute Bagging is propose...

Pattern recognition systems have been widely used in adversarial classification tasks like spam filtering and intrusion detection in computer networks. In these applications a malicious adversary may successfully mislead a classifier by “poisoning” its training data with carefully designed attacks. Bagging is a well-known ensemble construction method, where each classifier in the ensemble is tr...

The authorship attribution literature demonstrates the difficulty to design classifiers that outperform simple strategies such as linear classifiers operating on bag of features representation of documents. To overcome this difficulty we propose to use Bagging techniques that rely on learning classifiers on different random subsets of features, then to combine their decision by making them vote...

Boosting and Bagging, as two representative approaches to learning classiier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build diierent classiiers using a base learning algorithm by changing the distribution of the training set. Sasc, as a diierent type of committee learning method, can also signiicantly reduce the error rate of decision t...

Missing values are an unavoidable issue of many real-world datasets. Dealing with missing values is an essential requirement in classification problem, because inadequate treatment with missing values often leads to large classification errors. Some classifiers can directly work with incomplete data, but they often result in big classification errors and generate complex models. Feature selecti...

Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments with decision tree and neural network classifiers on various datasets show that, given the same size partitions and bags, disjoint partitions result in performance equivalent to, or better tha...

در این مقاله یک مدل ریاضی برای مسئله سیستم تولیدی همکارانه ساخت بر اساس سفارش با رعایت انصاف تخصیص بار‌های تولید طراحی شده است. اهداف اصلی مدل، کمینه‌سازی هزینه‌‌های کل و حداکثر استفاده از منابع به‌منظور عادلانه شرایط عدم­قطعیت کنترل پارامتر‌های غیرقطعی روش برنامه‌ریزی فازی ‌شده نتایج نشان می‌دهد افزایش نرخ عدم‌قطعیت، می­یابد. ازآنجاکه ظرفیت کارخانه‌ها ثابت است، مقدار تقاضا، هر کارخانه نیز می­ی...