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Multi-Class Classification from Single-Class Data with Confidences
[article]
2021
arXiv
pre-print
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Can we learn a multi-class classifier from only data of a single class? ...
We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee ...
Firstly, we propose an unbiased risk estimator for multi-class classification from data of a single class with confidences and provided the estimation error analysis. ...
arXiv:2106.08864v1
fatcat:d7lo7emctzctrpphs4gropwe2i
A Multi-path Strategy for Hierarchical Ensemble Classification
[chapter]
2014
Lecture Notes in Computer Science
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A solution to the multi-class classification problem is proposed founded on the concept of an ensemble of classifiers arranged in a hierarchical binary tree formation. ...
The conjectured advantage offered is that the confidence values associated with this form of classifier can be used to inform the proposed multi-path strategy. ...
single class label c taken from a set of disjoint class labels C. ...
doi:10.1007/978-3-319-08979-9_16
fatcat:4trpxyp7x5hdbafmwubuupdi2y
PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining
2016
International Journal of Computer Applications
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The existing methodologies can't cope of with such complex problems. This paper presents classification rule mining as a multi-objective problem rather than a single objective one. ...
In the rule extraction phase, a large number of classification rules are extracted from training data. This phase is based on two rule evaluation criteria: support (coverage) and confidence. ...
Partial classification is the classification of a particular single class from all the other classes. Similar formulation to [20] were used to search for Pareto-optimal association rules [21] . ...
doi:10.5120/ijca2016908697
fatcat:4jvvnqkxufhxxl34ijh3kd6f44
CLASSIFICATION ON MULTI-LABEL DATASET USING RULE MINING TECHNIQUE
2014
International Journal of Research in Engineering and Technology
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Most recent work has been focused on associative classification technique. Most research work of classification has been done on single label data. ...
Multi-label classification is an extension of single-label classification, and its generality makes it more difficult to solve compare to single label. ...
Whilst single-label classification, which assigns each rule in the classifier the most obvious class label, has been widely studied [9] little work has been conducted on multi-label classification. ...
doi:10.15623/ijret.2014.0306028
fatcat:y3rb7vmrpfdsna5roq72nmd4s4
MARRYING DEEP LEARNING AND DATA FUSION FOR ACCURATE SEMANTIC LABELING OF SENTINEL-2 IMAGES
2021
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. ...
In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models ...
map with the confidence score = 1; otherwise this pixel is rejected from the classification result.
2. ...
doi:10.5194/isprs-annals-v-3-2021-101-2021
fatcat:efjgs32crrbk3orgwh5q3et5aa
Doppler Spectrum Classification with CNNs via Heatmap Location Encoding and a Multi-head Output Layer
[article]
2019
arXiv
pre-print
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We analyze example images that fall outside of our proposed classes to show our confidence metric can prevent many misclassifications. ...
Our algorithm achieves 96% accuracy on a test set drawn from a separate clinical site, indicating that the proposed method is suitable for clinical adoption and enabling a fully automatic pipeline from ...
There are 9 classes instead of 10 here because with a single classification head only one NO class is needed. ...
arXiv:1911.02407v2
fatcat:aavziceezffzthlb5cbbz3mhkm
Multiple labels associative classification
2005
Knowledge and Information Systems
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In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multilabel associative classification approach (MMAC). ...
Results for 19 different data sets from the UCI data collection and nine hyperheuristic scheduling runs show that the proposed approach is an accurate and effective classification technique, highly competitive ...
Suhail Hamoud for his help in preparing the scheduling data sets and K. Chakhlevitch for supplying us with the original versions of the scheduling data sets. Multiple labels associative classification ...
doi:10.1007/s10115-005-0213-x
fatcat:o5v6vnaegncovbefj6kddfaw4m
Multi-label rules for phishing classification
2015
Applied Computing and Informatics
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Generating multi-label rules in associative classification (AC) from single label data sets is considered a challenging task making the number of existing algorithms for this task rare. ...
This algorithm discovers rules associated with a set of classes from single label data that other current AC algorithms are unable to induce. ...
In a single label classification, each training case in the input data is associated with only one class. ...
doi:10.1016/j.aci.2014.07.002
fatcat:lriyxszokzc7bjqgh3ciqk7e2u
Feature Selection for Multi-label Document Based on Wrapper Approach through Class Association Rules
2017
International Journal on Advanced Science, Engineering and Information Technology
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The experiments carried out with benchmark datasets revealed that the Naïve Bayes Multi-label (NBML) classifier with business dataset scored an average precision of 87.9% upon using a 0.1 % of minimum ...
Then, this study conducted an evaluation of seven minimum confidence thresholds. Additionally, Class Association Rules (CARs) represents the wrapper approach for this evaluation. ...
However, the Naive Bayes classifier in multi-label classification is adapted to deal with multi-label data directly. ...
doi:10.18517/ijaseit.7.2.1040
fatcat:qbrxsopr5jh3lkb7hq6zvjtc2e
Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class
2007
IEEE International Fuzzy Systems conference proceedings
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It will also be shown that multi-model architecture can outperform conventional single-model architecture ('classical' fuzzy classification models) for all data sets with respect to prediction accuracy ...
This analysis will include the comparison of evolving single-and multi-model fuzzy classifiers with conventional batch modelling approaches with respect to achieved prediction accuracy on new online data ...
CONCLUSION AND OUTLOOK Two variants for evolving fuzzy classification schemes were presented, FLEXFIS-Class SM based on single-model architecture and FLEXFIS-Class MM based on multi-model architecture. ...
doi:10.1109/fuzzy.2007.4295393
dblp:conf/fuzzIEEE/LughoferAZ07
fatcat:kq57n72ornc55psqkxixqhkyty
Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network
2012
International Journal of Computer Applications
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SOM is a class of typical artificial neural networks (ANN) with supervised learning which has been widely used in classification tasks. ...
Classification and Association rule mining are two basic tasks of Data Mining. Classification rules mining finds rules that partition the data into disjoint sets. ...
After the drawing out multi-relational CRCIs in a relational database, it's time to generate classification rules from CRCIs. ...
doi:10.5120/4979-7237
fatcat:w2wbo6lpwbehnpzxsqny44c5yi
Semi-supervised multi-label image classification based on nearest neighbor editing
2013
Neurocomputing
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In order to reduce this negative effect, the nearest neighbor data editing technique is introduced to semi-supervised multi-label classification, and thus an algorithm named Multi-Label Self-Training with ...
confident samples during the course of semi-supervised learning. ...
To the best of our knowledge, there is little research work about semi-supervised multi-label classification with data editing technique. ...
doi:10.1016/j.neucom.2013.03.011
fatcat:w45ppwtllzdphdohasniitpzyq
Multi-represented kNN-Classification for Large Class Sets
[chapter]
2005
Lecture Notes in Computer Science
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To cope with all these requirements, we introduce a novel approach to classification of multi-represented objects that is capable to distinguish large numbers of classes. ...
Therefore, classification of these complex objects is an important data mining task that yields several new challenges. In many applications, the data objects provide multiple representations. ...
Conclusions In this paper, we proposed a novel approach for classifying multi-represented data objects into flat class-systems with many classes. ...
doi:10.1007/11408079_45
fatcat:iikuuwdfsfe4thztahavgmte7e
A Cross-Conformal Predictor for Multi-label Classification
[chapter]
2014
IFIP Advances in Information and Communication Technology
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Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. ...
CP complements the predictions of machine learning algorithms with reliable measures of confidence. ...
Introduction Most machine learning research on classification deals with problems in which each instance is associated with a single class y from a set of classes {Y 1 , . . . , Y c }. ...
doi:10.1007/978-3-662-44722-2_26
fatcat:ihxralo26fg3fop2qpom3mqwgy
3D Terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology
[article]
2012
arXiv
pre-print
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Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution. ...
3D point clouds of natural environments relevant to problems in geomorphology often require classification of the data into elementary relevant classes. ...
by the single large scale classifier, are correctly retrieved with the multi-scale approach. ...
arXiv:1107.0550v3
fatcat:l7chazuetrgbpfktxtyj6cltnu
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