A method for the local and global interpretation of a black-box model on basis well-known generalized additive models is proposed. It can be viewed as an extension or modification algorithm using neural model. The based ensemble gradient boosting machines (GBMs) such that each GBM learned single feature produces shape function feature. composed weighted sum separate GBMs resulting functions whi...

This paper presents a novel ensemble learning approach to resolving German pronouns. Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one. Experiments show that this approach is superior to a single decision-tree classifier. Furthermore, we present a standalone system that resolves pronouns in unannotated text by usin...

An ensemble contains a number of learners which are usually called base learners. The generalization ability of an ensemble is usually much stronger than that of base learners. Actually, ensemble learning is appealing because that it is able to boost weak learners which are slightly better than random guess to strong learners which can make very accurate predictions. So, “base learners” are als...

One of crucial issue in the design of combinational classifier systems is to keep diversity in the results of classifiers to reach the appropriate final result. It's obvious that the more diverse the results of the classifiers, the more suitable final result. In this paper a new approach for generating diversity during creation of an ensemble together with a new combining classifier system is p...

Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain–computer in...

As the salaries of baseball players continue to skyrocket and with the ever-increasing popularity of fantasy baseball, the desire for more accurate predictions of players’ future performances is building both for baseball executives and baseball fans. While most existing work in performance prediction uses purely statistical methods, this thesis showcases research in combining multiple machine ...

A new optimization technique is proposed for classifiers fusion — Cooperative Coevolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm — cooperative coevolution. It can be used as a wrapper over any kind of weak algorithms, learning procedures and fusion functions, for both classification and regression tasks. Experiments on the real-world prob...

While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within th...

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new...

The classification of data with imbalanced class distributions has posed a significant drawback in the performance attainable by most well-developed classification systems, which assume relatively balanced class distributions. This problem is especially crucial in many application domains, such as medical diagnosis, fraud detection, network intrusion, etc., which are of great importance in mach...