Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …
impact on every industry and research discipline. At the core of this revolution lies the tools …
[HTML][HTML] A recent overview of the state-of-the-art elements of text classification
MM Mirończuk, J Protasiewicz - Expert Systems with Applications, 2018 - Elsevier
The aim of this study is to provide an overview the state-of-the-art elements of text
classification. For this purpose, we first select and investigate the primary and recent studies …
classification. For this purpose, we first select and investigate the primary and recent studies …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
A dynamic ensemble learning algorithm for neural networks
This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …
The choice of scaling technique matters for classification performance
Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …
How complex is your classification problem? a survey on measuring classification complexity
Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …
be effective predictors in a number of meta-analyses. Among them, measures of …
[PDF][PDF] Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
P Pławiak, UR Acharya - Neural Comput. Appl, 2020 - researchgate.net
The heart disease is one of the most serious health problems in today's world. Over 50
million persons have cardiovascular diseases around the world. Our proposed work based …
million persons have cardiovascular diseases around the world. Our proposed work based …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance
The capability of distinguishing between small objects when manipulated with hand is
essential in many fields, especially in video surveillance. To date, the recognition of such …
essential in many fields, especially in video surveillance. To date, the recognition of such …
Offline handwritten signature verification—Literature review
LG Hafemann, R Sabourin… - … conference on image …, 2017 - ieeexplore.ieee.org
The area of Handwritten Signature Verification has been broadly researched in the last
decades, but remains an open research problem. The objective of signature verification …
decades, but remains an open research problem. The objective of signature verification …