The experimental results show that an improvement is possible with the proposed technique in most of the real world classification problems.
Multi-instance learning (MIL) is one of promising paradigms in the supervised learning aiming to handle real world classification problems where a classification target contains several featured sections, e.g., an image typically contains several salient regions.
The classifier performance is calculated using Algorithm 2. Before we present our numerical results demonstrating the performance of the distributed classifier, let us discuss the motivation for using nonseparable data sets as representative examples of real world classification problems.
Datasets with missing values are frequent in real-world classification problems.
The proposed method was successfully applied to solving artificial and real-world classification problems: XOR and microRNA expression patterns.
While clustering the training data may sometimes gather examples from the same class in their own cluster, we do not believe that would be the case for most real-world classification problems.
In contrast to other methods, the proposed method is especially suitable for a large-scale real-world classification problems although it is easily scaled to a small training set while preserving a good performance.
In order to study the usefulness of the proposed algorithm, BMO is applied to weight training of ANNs for solving three real-world classification problems, namely, Iris flower, Wisconsin breast cancer, and Pima Indian diabetes.
Finally, the proposed algorithm is tested on 14 real-world classification problems from the UCI machine learning repository, and experimental results illustrate that the algorithm is able to produce RBFNN models that have better prediction accuracies and simpler structures than conventional algorithms of classification.
Many real-world classification problems involve multidimensional inputs.
