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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. ...
representations and on multiple representations combined by sum rule [15] ; kNN classifiers combined by sum rule. single-represented data. ...
doi:10.1007/11408079_45
fatcat:iikuuwdfsfe4thztahavgmte7e
A New Hybrid KNN Classification Approach based on Particle Swarm Optimization
2020
International Journal of Advanced Computer Science and Applications
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K-Nearest Neighbour algorithm is widely used as a classification technique due to its simplicity to be applied on different types of data. ...
solve the presence of outliers by taking the result of the first phase and apply on it a new proposed scored K-Nearest Neighbour technique. ...
More enhancements were done by integration of multiple algorithms, Bahramian and Nikravanshalmani [6] proposed a new classification algorithm based on feature selection with genetic algorithm and combination ...
doi:10.14569/ijacsa.2020.0111137
fatcat:coxby3btmngzbktqp3oncvpqe4
Classification of Hand Gestures from Wearable IMUs using Deep Neural Network
[article]
2020
arXiv
pre-print
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Other Versions
2018
2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)
unpublished
doi:10.1109/icicct.2018.8473301 fatcat:agr637pge5exjdjwqwqrbreshe
doi:10.1109/icicct.2018.8473301 fatcat:agr637pge5exjdjwqwqrbreshe
The predicted outputs are analyzed in the form of classification accuracies which are then compared to the conventional classification schemes of SVM and kNN. ...
Training of the network is carried out by feed-forward computation of the input features followed by the back-propagation of errors. ...
Reduction in the dimensionality serves as an important step in order to predict the values for multiple observations with similar characteristic traits. ...
arXiv:2005.00410v1
fatcat:7pn7nimqgbh4ng5hrvlvqxr3bu
Multiple classifier systems for automatic sleep scoring in mice
2016
Journal of Neuroscience Methods
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Ethical Standards: I have read and have abided by the statement of ethical standards for manuscripts submitted to the Journal of Neuroscience Methods. ...
Acknowledgements This work was supported by The Defense Advanced Research Projects Agency (government contract/grant number W911NF1010066). ...
KNN-RS was also more accurate than KNN (p<0.001) (Fig.3B) , reducing errors by 25%. The multiple classifier system (MCS) had an error rate of 0.049, a reduction of 9.4% from SVM (p<0.001) (Fig.4) . ...
doi:10.1016/j.jneumeth.2016.02.016
pmid:26928255
pmcid:PMC4833589
fatcat:qquidotijvhwxodtylficc44y4
Graph regularized implicit pose for 3D human action recognition
2016
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)
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Action classification of a test sequence {û 1,test , . . . ,û T,test } with T frames is then done by the frame-based KNN approach c ? ...
Frame Class Confidence and Classification As we have argued, the transformed featuresû in the matrix U should be well-suited for a KNN classifier. ...
doi:10.1109/apsipa.2016.7820717
dblp:conf/apsipa/KerolaIS16
fatcat:66he7tvgtrbz5dfzz4zbdxtvyq
A Review of various KNN Techniques
2017
International Journal for Research in Applied Science and Engineering Technology
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Some techniques are non-structure based like Weighted KNN, Model based KNN, distance based KNN, Class confidence weighted KNN, Dynamic weighted KNN, Clustering based KNN, and Pre-classification based KNN ...
K-Nearest Neighbor is highly efficient classification algorithm due to its key features like: very easy to use, requires low training time, robust to noisy training data, easy to implement, but alike other ...
A weighted KNN model-based data reduction and classification algorithm finds some more meaningful representatives to replace the original dataset for further classification proposed by Xuming Huang et ...
doi:10.22214/ijraset.2017.8166
fatcat:ze5cmpcgsrgund5uwsbujiuxza
Hyperspectral Image Classification by Using Pixel Spatial Correlation
[chapter]
2013
Lecture Notes in Computer Science
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This paper introduces a hyperspectral image classification approach by using pixel spatial relationship. ...
Comparisons with the state-of-the-art methods demonstrate that the proposed method can effectively investigate the spatial relationship among pixels and achieve better hyperspectral image classification ...
This work was supported by NUS-Tsinghua Extreme Search (NExT) project under the grant number: R-252-300-001-490. ...
doi:10.1007/978-3-642-35725-1_13
fatcat:poya3tmuxnhbxnbnrjyyqwer6q
Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics
2020
Scientific Reports
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The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. ...
In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods ...
The classification performance of lung cancer subtypes with dimensionality reduction. ...
doi:10.1038/s41598-020-62803-4
pmid:32246031
fatcat:lxb737l7u5cejg4du4o2sx5mui
Supervised Feature Space Reduction for Multi-Label Nearest Neighbors
[chapter]
2017
Lecture Notes in Computer Science
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In this article, we skirt the explicit multi-objective formulation with a novel linear reduction method for optimizing the ML-kNN classification performances. ...
Thirdly, the performances of the original ML-kNN are always improved by at least one dimensionality reduction approach. ...
doi:10.1007/978-3-319-60042-0_21
fatcat:ywdnz26pc5f5vfuz653dvz7ozi
Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data
2014
Remote Sensing
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This classification result is also compared with that obtained by replacing the base classifier by LDA (Linear Discriminant Analysis), kNN (k Nearest Neighbor) and SVM (Support Vector Machine). ...
Sens. 2014, 6 11226 with improvement to 91.2% using the ensemble method. ...
Acknowledgments This research was funded by GeoDigital International Inc., Ontario Centres for Excellence, and a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada. ...
doi:10.3390/rs61111225
fatcat:r42k7ypejza4bodeburc2vka5a
An Experimental Evaluation of Fault Diagnosis from Imbalanced and Incomplete Data for Smart Semiconductor Manufacturing
2018
Big Data and Cognitive Computing
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Furthermore, a novel data imputation approach, namely "In-painting KNN-Imputation" is introduced and is shown to outperform the common data imputation technique. ...
The results show the capability of each classifier, feature selection method, data generation method, and data imputation technique, with a full analysis of their respective parameter optimizations. ...
The task of imputation is done using KNN regression. To begin with, the missing data point (X 0 , Y 0 ) with the highest confidence and familiarity is found. ...
doi:10.3390/bdcc2040030
fatcat:yg4vph3av5gdjfkwfb3ttyuyae
Coarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
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These two steps can also be considered as effective "sample pruning" and "feature pursuing + kNN/template matching", respectively. ...
High detection sensitivity with desirably low false positive (FP) rate is critical for a CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. ...
However we perform sample pruning by selecting data upon their classification scores/confidences of a learned parametric model that is well studied, more robust and stable, compared with nearest neighbor ...
doi:10.1145/2063576.2064004
dblp:conf/cikm/LuLYYH11
fatcat:74nsydvy7vbyxbk3kaoejjgjoi
kNN based image classification relying on local feature similarity
2010
Proceedings of the Third International Conference on SImilarity Search and APplications - SISAP '10
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In this paper, we propose a novel image classification approach, derived from the kNN classification strategy, that is particularly suited to be used when classifying images described by local features ...
With the use of local features generated over interest points, we revised the single label kNN classification approach to consider similarity between local features of the images in the training set rather ...
In Section 4 we present an image similarity measures relying on local features to be used with a kNN classification algorithm. ...
doi:10.1145/1862344.1862360
dblp:conf/sisap/AmatoF10
fatcat:lencmytdungxhjdn65oo4tf5j4
Beam-Influenced Attribute Selector for Producing Stable Reduct
2022
Mathematics
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To generate the reduct with higher stability, a novel beam-influenced selector (BIS) is designed based on the strategies of random partition and beam. ...
Comprehensive experiments using 16 UCI data sets show the following: (1) the stability of the derived reducts may be significantly enhanced by using our selector; (2) the reducts generated based on the ...
Take the "Sonar (ID: 13)" data set as an example, over the raw data, the values with respect to classification accuracies based on the KNN classifier of reducts obtained by AGAR, DAR, DGAR, ESAR, FBGSAR ...
doi:10.3390/math10040553
fatcat:w42xldjrlvbprf37yw7p45fhzu
A multilevel features selection framework for skin lesion classification
2020
Human-Centric Computing and Information Sciences
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In this work, we come up with a novel framework for skin lesion classification, which integrates deep features information to generate most discriminant feature vector, with an advantage of preserving ...
, and by utilizing less than 3% features. ...
Acknowledgements Research is funded by Deanship of Scientific Research at University of Ha'il.
Authors' contributions ...
doi:10.1186/s13673-020-00216-y
fatcat:dxom62b6gfaxbhoqqoy4x7hqhy
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