Autonoml: Towards an integrated framework for autonomous machine learning
Over the last decade, the long-running endeavour to automate high-level processes in
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …
Ontology-based meta-mining of knowledge discovery workflows
This chapter describes a principled approach to meta-learning that has three distinctive
features. First, whereas most previous work on meta-learning focused exclusively on the …
features. First, whereas most previous work on meta-learning focused exclusively on the …
[PDF][PDF] Meta-learning with landmarking: A survey
Everyday large amount of data is generated. Data mining is a technique that extracts
significant information from raw data using different classifiers. Meta-learning is machine …
significant information from raw data using different classifiers. Meta-learning is machine …
[PDF][PDF] A novel holistic disease prediction tool using best fit data mining techniques
SA Diwani, ZO Yonah - Int. J. Com. Dig. Sys, 2017 - academia.edu
Given that, today, the healthcare ecosystem is an information rich industry, there is an
increasing demand for data mining (DM) tools to improve the quantity and quality of …
increasing demand for data mining (DM) tools to improve the quantity and quality of …
Meta-learning framework for prediction strategy evaluation
The paper presents a framework which brings together the tools necessary to analyze new
problems and make predictions related to the learning algorithms' performance and …
problems and make predictions related to the learning algorithms' performance and …
Algorithm selection via meta-learning and active meta-learning
To find most suitable classifier is possible either through cross-validation, which suffers from
computational cost or through expert advice which is not always feasible to have. Meta …
computational cost or through expert advice which is not always feasible to have. Meta …
Evolutionary approach for automated component-based decision tree algorithm design
This paper proposes a framework for automated design of component-based decision tree
algorithms. These algorithms are being constructed by interchanging components extracted …
algorithms. These algorithms are being constructed by interchanging components extracted …
Meta-Learning in Supervised Machine Learning
A Al Masud, S Hossain, M Rifa, F Akter… - 2022 14th …, 2022 - ieeexplore.ieee.org
In the present digital era, a popular use of Machine learning is knowledge mining from big
data. Machine learning is the sub-branch of Artificial Intelligence (AI) that extracts rules …
data. Machine learning is the sub-branch of Artificial Intelligence (AI) that extracts rules …
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
Over the last decade, the long-running endeavour to automate high-level processes in
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …
Novel algorithm to measure consistency between extracted models from big dataset and predicting applicability of rule extraction
Many advancement is made in recent days and number of techniques are proposed by
different researchers for processing and extracting knowledge from big data. But to evaluate …
different researchers for processing and extracting knowledge from big data. But to evaluate …