The imbalanced class distribution is one of the main issue in data mining. This problem exists in multi class imbalance, when samples containing in one class are greater or lower than that of other classes. Most existing imbalance learning techniques are only designed and tested for two-class scenarios. The new negative correlation learning (NCL) algorithm for classification ensembles, called A...

In the construction industry, evaluating the financial status of a contractor is a challenging task due to the myriad of the input data as well as the complexity of the working environment. This article presents a novel hybrid intelligent approach named as Evolutionary Least Squares Support Vector Machine Inference Model for Predicting Contractor Default Status (ELSIM-PCDS). The proposed ELSIM-...

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply differe...

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tun...

Problems of class imbalance appear in diverse domains, ranging from gene function annotation to spectra and medical classification. On such problems, the classifier becomes biased in favour of the majority class. This leads to inaccuracy on the important minority classes, such as specific diseases and gene functions. Synthetic oversampling mitigates this by balancing the training set, whilst av...

Customer churn is a main concern of most firms in all industries. The aim of customer churn prediction is detecting customers with high tendency to leave a company. Although, many modeling techniques have been used in the field of churn prediction, performance of ensemble methods has not been thoroughly investigated yet. Therefore, in this paper, we perform a comparative assessment of the perfo...

<span>The first year of an engineering student was important to take proper academic planning. All subjects in the were essential for basis. Student performance prediction helped academics improve their better. Students checked by themselves. If they aware that are low, then could make some improvement better performance. This research focused on combining oversampling minority class data...

In order to improve the construction quality of tourism management projects, this paper applies data mining algorithm management, and analyzes SMOTE algorithm. According improvement direction, proposes two improved algorithms, KM-SMOTE RM-SMOTE, uses clustering preprocess minority set. Moreover, on basis, establishes clusters obtains cluster centers. The deficiencies fuzzy positive negative cla...

Chemical oxygen demand (COD) is one of the indicators used to monitor level pollution in surface water. To recycle agricultural water resources, it crucial monitor, a timely manner, whether COD exceeds control standard. A diagnostic model was developed using visible near-infrared spectroscopy (Vis-NIR) combined with partial least squares discriminant analysis (PLS–DA). total 127 samples were co...