In this paper, we present BENCH (Biclusteringdriven ENsemble of Classifiers), an algorithm to construct an ensemble of classifiers through concurrent feature and data point selection guided by unsupervised knowledge obtained from biclustering. BENCH is designed for underdetermined problems. In our experiments, we use Bayesian Belief Network (BBN) classifiers as base classifiers in the ensemble;...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

The use of network connected devices has grown exponentially in recent years revolutionizing our daily lives. However, it also attracted the attention cybercriminals making attacks targeted towards these increase not only numbers but sophistication. To detect such attacks, a Network Intrusion Detection System (NIDS) become vital component applications. produce large scale high-dimensional data ...

Cancer diagnosis using machine learning algorithms is one of the main topics research in computer-based medical science. Prostate cancer considered reasons that are leading to deaths worldwide. Data analysis gene expression from microarray and soft computing a useful tool for detecting prostate diagnosis. Even though traditional methods have been successfully applied cancer, large number attrib...

Diabetes mellitus is a metabolic disease that ranked among the top 10 causes of death by world health organization. During last few years, an alarming increase observed worldwide with 70% rise in since 2000 and 80% male deaths. If untreated, it results complications many vital organs human body which may lead to fatality. Early detection diabetes task significant importance start timely treatme...

<span>Heart disease is a prevalent global health concern, necessitating early detection to save lives. Machine learning has revolutionized medical research, prompting the investigation of boosting algorithms for heart prediction. This study employs three datasets from University California Irvine (UCI) repository: Cleveland, Statlog, and Long Beach, with 14 features each. Recursive featur...

Exploring and analyzing large datasets has become an active research area in the field of data mining in the last two decades. There had been several approaches available in the literature to investigate the large datasets that comprise of millions of data. The most important data mining approaches involved in this task are preprocessing, feature selection and classification. All the three appr...

The domain adaptation problem arises often in the field of sentiment classification. There are two distinct needs in domain adaptation, namely labeling adaptation and instance adaptation. Most of current research focuses on the former one, while neglects the latter one. In this work, we propose a joint approach, named feature ensemble plus sample selection (SS-FE), which takes both types of ada...