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About 10,000 papers for “classification”

Multilabel classification Imbalanced classification

Francisco Charte A, Antonio J. Rivera B, María J. Del Jesus B et al. · 2015

a b s t r a c t The purpose of this paper is to analyze the imbalanced learning task in the multilabel scenario, aiming to accomplish two different goals. The first one is to present specialized measures directed to assess the imbalance level in multilabel datasets (MLDs). Using these measures we will be able to conclude which MLDs are imbalanced, and therefore would need an appropriate treatment. The second objective is to propose several algorithms designed to reduce the imbalance in MLDs in a classifier-independent way, by means of resampling techniques. Two different approaches to divide the instances in minority and majority groups are studied. One of them considers each label combination as class identifier, whereas the other one performs an individual evaluation of each label imbalance level. A random undersampling and a random oversampling algorithm are proposed for each approach, giving as result four different algorithms. All of them are experimentally tested and their effectiveness is statistically evaluated. From the results obtained, a set of guidelines directed to show when these methods should be applied is also provided. & 2015 Elsevier B.V. All rights reserved. 1.

Cited by 0

emiresenov/PRPD-Classification: prpd-classification

Emir Esenov · Zenodo (CERN European Organization for Nuclear Research) · 2025

Classification of Phase-Resolved Partial Discharge Images from Electrical Generators using Convolutional Neural Networks

Cited by 0Open access

Ethnic group classification: census classification.

Sobha Sivaprasad (318118), Bhaskar Gupta (21544), Martin C. Gulliford (187134) et al. · Figshare · 2015

<p>South Asian in this study is defined as Indians, Pakistanis, Bangladeshi and Srilankan.</p>

Cited by 0Open access

45. Stellar Classification (Classification Stellaire)

O Levalo, M Gerbaldi, R Bartaya et al. · Transactions of the International Astronomical Union · 1997

This report covers research in the field of stellar classification in the period July 1993 to June 1996. It is divided into several sections which were written by experts in each subfield. I want to thank them for their effort and cooperation. To conserve space, all references are given with only one name followed by a + sign if there are additional authors.

Cited by 1Open access

Classification results of DDI classification.

Qingyu Chen (3142713), Kyubum Lee (3229017), Shankai Yan (6993716) et al. · Figshare · 2020

<p>SOTA: state-of-the-art. P: Precision. R: Recall. The SOTA results are extracted from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1007617#pcbi.1007617.ref063" target="_blank">63</a>]. Direct comparison: the results of embeddings using the same method (cbow) and same hyperparameters. Different embedding methods: the results of BioConceptVec (skip-gram), BioConceptVec (GloVe) and BioConceptVec (fastText). The highest results of each section are marked as bold.</p>

Cited by 0Open access

Consistent classification and classification confidence.

Matthew A. Care (281365), Sharon Barrans (281366), Lisa Worrillow (281367) et al. · Figshare · 2015

<p>The classes assigned by 31 tested classifiers for the GSE10846 CHOP data set are shown along with published classes in GEO and those assigned by the LPS classifier (GCB = blue, Type-III = green, and ABC = yellow). Samples are vertically ordered by class assigned by the meta-classifier LMT_J48_RF100_SMO (later referred to as “DAC”); this meta-classifier assigns confidence scores for each class, and the class with highest confidence is selected for each sample. Within each class samples are ranked by classification confidence. At either extreme, samples are ordered from high to low confidence GCB, and from low to high confidence ABC. In the Type-III category high confidence cases are shown centrally, flanked by lower confidence Type-III cases. On either side the latter are ordered by GCB or ABC signal (identified by GCB or ABC being the second highest classification confidence). The first column (labelled with black bar and red 5) identifies the classes assigned by LMT_J48_RF100_SMO, followed by results obtained for 30 other machine-learning classifiers, with the classes assigned for each case in the appropriate color. Classifiers are ranked (number above each column) from left to right according to the significance of survival separation between assigned ABC and GCB classes; note that LMT_J48_RF100_SMO was selected as the reference based on overall performance across multiple data sets, and in this data set is ranked 5<sup>th</sup> (shown in red) for survival separation. On the far right the published class assignments linked in GEO to the data set (GEO class, orange bar) and classes assigned by the LPS classifier using either a 0.8 or 0.9 p-value threshold classes are shown (dark gray bars respectively).</p>

Cited by 0Open access

Audio Classification: Environmental sounds classification

Baljinder Kaur, Jaskirat Singh · HAL (Le Centre pour la Communication Scientifique Directe) · 2021

Recent advancements in the field of machine learning have led to a growing interest in many classification problems, especially involving data in the form of images, video, and audio files. One of the prominent classification problems is to classify sounds and to predict the category of that sound. Some of the applications where such a classification model can be applied in the real world are security systems, classifying music clips to identify the genre of the music, classifying different environmental sounds, speaker detection, and verification. Audio classification is the task of analyzing different audio signals. In this paper, we provide a brief overview of the area of audio classification, describing its system, various modules of feature extraction and modeling, applications, underlying techniques, and some indications of performance. Following this overview, we will discuss some of the strengths and weaknesses of current classification technologies and outline some potential future trends in research, development, and applications. We paid close attention to the inputs, network structures, temporal pooling strategies, and objective functions as these are the fundamental components of many audio classification subtasks. The paper concludes with discussions on future trends and research opportunities in this area.

Cited by 2Open access