The main purpose of the present study is to use three state-of-the-art data mining
techniques, namely, logistic model tree (LMT), random forest (RF), and classification and …

Sentiment analysis is an important research direction of natural language processing, text
mining and web mining which aims to extract subjective information in source materials. The …

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a
clustering algorithm based on k-mean paradigm that works well for data with mixed numeric …

Objective To evaluate the performance of quantitative computed tomography (CT) texture
analysis using different machine learning (ML) classifiers for discriminating low and high …

The massive collection of data via emerging technologies like the Internet of Things (IoT)
requires finding optimal ways to reduce the created features that have a potential impact on …

Machine learning classifiers have recently emerged as a way to predict the introduction of
bugs in changes made to source code files. The classifier is first trained on software history …

In this study, a hybrid genetic algorithm is adopted to find a subset of features that are most
relevant to the classification task. Two stages of optimization are involved. The outer …

Objective To develop externally validated, reproducible, and generalizable models for
distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning …

Software defect prediction aims to determine whether a software module is defect-prone by
constructing prediction models. The performance of such models is susceptible to the high …

Multimedia data mining, particularly feature selection (FS), has been successfully applied in
recent classification and recognition works. However, only a few studies in the contemporary …