The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In recent years there has been an increasing interest in creating ens...

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the integration procedure in the ensemble should properly utilize the ensemble diversity. In this paper...

Commercial and residential buildings are responsible for a substantial portion of total global energy consumption and as a result make a significant contribution to global carbon emissions. Hence, energy-saving goals that target buildings can have a major impact in reducing environmental damage. During building operation, a significant amount of energy is wasted due to equipment and human-relat...

Ensemble of classifiers increases the performance of the classification since the decision of many experts are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope ...

Background Due to rich information embedded in published articles, literature review has become an important aspect of research activities in the biomedical domain. Machine Learning (ML) techniques have been explored to retrieve relevant articles from a large literature archive (i.e., classifying articles into relevant and irrelevant classes), and to accelerating the literature review process. ...

The problem of multi-class classification is explored using heterogeneous ensemble classifiers. Heterogeneous ensembles classifiers are defined as ensembles, or sets, of classifier models created using more than one type of classification algorithm. For example, the outputs of decision tree classifiers could be combined with the outputs of support vector machines (SVM) to create a heterogeneous...

It is known that an ensemble of classifiers can outperform a single best classifier if classifiers in the ensemble are sufficiently diverse (i.e., their errors are as much uncorrelated as possible) and accurate. We study ensembles of nearest neighbours for cancer classification based on gene expression data. Such ensembles have been rarely used, because the traditional ensemble methods such as ...

In this paper, we investigate the impact of the non-numerical information on exchange rate changes and that of ensemble multiple classifiers on forecasting exchange rate between U.S. dollar and Japanese yen. We first engage the fuzzy comprehensive evaluation model to quantify the nonnumerical fundamental information. We then design a single classifier, addressing the impact of exchange rate cha...

Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the training process. However, the reliance on labeled data precludes the applic...

Diversity in ensembles is the key to improved accuracy. Six pair wise diversity measures have been studied and Plain Disagreement and Kappa coefficient are being recommended for ensemble construction as they exhibit high correlation with error reduction. We have also verified that correlation between diversity and error reduction increases with an increase in the ensemble size. This study also ...