Feature selection is the process of identifying relevant features in the dataset and discarding everything else as irrelevant and redundant. Since feature selection reduces the dimensionality of the data, it enables the learning algorithms to operate more effectively and rapidly. In some cases, classification performance can be improved; in other instances, the obtained classifier is more compa...

Ensemble learning deals with methods which employ multiple learners to solve a problem The generalization ability of an ensemble is usually significantly better than that of a single learner, so ensemble methods are very attractive, at the same time feature selection process of ensemble technique has important role of classifier. This paper, presents the analysis on classification technique of ...

Feature selection methods are essential to identify a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies showed the defectiveness in terms of specific biases of single feature selection methods, whereas an ensemble of feature selection techniques has the advantage to alleviate and com...

High dimensionality and class imbalance are two main problems that affect the quality of training datasets in software defect prediction, resulting in inefficient classification models. Feature selection and data sampling are often used to overcome these problems. Feature selection is a process of choosing the most important attributes from the original data set. Data sampling alters the data s...

Ensembles of classiiers have been shown to be very eeective for case-based classiication tasks. The vast majority of ensemble construction algorithms use the complete set of features available in the problem domain for the ensemble creation. Recent work on randomly selected subspaces for ensemble construction has been shown to improve the accuracy of the ensemble considerably. In this paper we ...

Abstract Intrusion detection systems get more attention to secure the computers and network systems. Researchers propose different intrusion using machine learning techniques. However, massive amount of data that contain irrelevant redundant features is still challenging The redundancy irrelevance may slow processing time decrease prediction performance. This paper proposes a Heterogeneous Ense...

Data mining (DM) is a scheme in which useful information extracted from the unstructured data. On basis of existing information, prediction analysis (PA) method used to forecast future possibilities. This investigate work arranged on premise foreseeing cardiac illness. To pre-process data, extract attributes, and classify are all steps forecasting coronary artery disease. projected hybrid model...