A comprehensive survey on the process, methods, evaluation, and challenges of feature selection
Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
Scenenn: A scene meshes dataset with annotations
Several RGB-D datasets have been publicized over the past few years for facilitating
research in computer vision and robotics. However, the lack of comprehensive and fine …
research in computer vision and robotics. However, the lack of comprehensive and fine …
Reduced kernel random forest technique for fault detection and classification in grid-tied PV systems
K Dhibi, R Fezai, M Mansouri, M Trabelsi… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
The random forest (RF) classifier, which is a combination of tree predictors, is one of the
most powerful classification algorithms that has been recently applied for fault detection and …
most powerful classification algorithms that has been recently applied for fault detection and …
Choosing software metrics for defect prediction: an investigation on feature selection techniques
The selection of software metrics for building software quality prediction models is a search‐
based software engineering problem. An exhaustive search for such metrics is usually not …
based software engineering problem. An exhaustive search for such metrics is usually not …
A survey of evaluation in music genre recognition
BL Sturm - International Workshop on Adaptive Multimedia …, 2012 - Springer
Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic
data, and other modalities. While reviews have been written of some of this work before, no …
data, and other modalities. While reviews have been written of some of this work before, no …
Access to ethnic music: Advances and perspectives in content-based music information retrieval
O Cornelis, M Lesaffre, D Moelants, M Leman - Signal Processing, 2010 - Elsevier
Access to digital music collections is nowadays facilitated by content-based methods that
allow the retrieval of music on the basis of intrinsic properties of audio, in addition to …
allow the retrieval of music on the basis of intrinsic properties of audio, in addition to …
A comparative study on the effect of feature selection on classification accuracy
Feature selection has become interest to many research areas which deal with machine
learning and data mining, because it provides the classifiers to be fast, cost-effective, and …
learning and data mining, because it provides the classifiers to be fast, cost-effective, and …
Effective fault prediction model developed using least square support vector machine (LSSVM)
Software developers and project teams spend considerable amount of time in identifying
and fixing faults reported by testers and users. Predicting defects and identifying regions in …
and fixing faults reported by testers and users. Predicting defects and identifying regions in …
An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes
Software fault prediction models are used to predict faulty modules at the very early stage of
software development life cycle. Predicting fault proneness using source code metrics is an …
software development life cycle. Predicting fault proneness using source code metrics is an …
Detecting hospital-acquired infections: a document classification approach using support vector machines and gradient tree boosting
C Ehrentraut, M Ekholm, H Tanushi… - Health informatics …, 2018 - journals.sagepub.com
Hospital-acquired infections pose a significant risk to patient health, while their surveillance
is an additional workload for hospital staff. Our overall aim is to build a surveillance system …
is an additional workload for hospital staff. Our overall aim is to build a surveillance system …