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Multi-represented kNN-Classification for Large Class Sets [chapter]

Hans-Peter Kriegel, Alexey Pryakhin, Matthias Schubert
2005 Lecture Notes in Computer Science  
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

Reem Kadry, Osama Ismael
2020 International Journal of Advanced Computer Science and Applications  
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]

Karush Suri, Rinki Gupta
2020 arXiv   pre-print
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

Vance Gao, Fred Turek, Martha Vitaterna
2016 Journal of Neuroscience Methods  
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

Tommi Kerola, Nakamasa Inoue, Koichi Shinoda
2016 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)  
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

Pooja Rani
2017 International Journal for Research in Applied Science and Engineering Technology  
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]

Yue Gao, Tat-Seng Chua
2013 Lecture Notes in Computer Science  
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

Chunyan Wang, Yijing Long, Wenwen Li, Wei Dai, Shaohua Xie, Yuanling Liu, Yinchenxi Zhang, Mingxin Liu, Yonghui Tian, Qiang Li, Yixiang Duan
2020 Scientific Reports  
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]

Wissam Siblini, Reda Alami, Frank Meyer, Pascale Kuntz
2017 Lecture Notes in Computer Science  
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

Connie Ko, Gunho Sohn, Tarmo Remmel, John Miller
2014 Remote Sensing  
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

Milad Salem, Shayan Taheri, Jiann-Shiun Yuan
2018 Big Data and Cognitive Computing  
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

Le Lu, Meizhu Liu, Xiaojing Ye, Shipeng Yu, Heng Huang
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
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

Giuseppe Amato, Fabrizio Falchi
2010 Proceedings of the Third International Conference on SImilarity Search and APplications - SISAP '10  
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

Wangwang Yan, Jing Ba, Taihua Xu, Hualong Yu, Jinlong Shi, Bin Han
2022 Mathematics  
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

Tallha Akram, Hafiz M. Junaid Lodhi, Syed Rameez Naqvi, Sidra Naeem, Majed Alhaisoni, Muhammad Ali, Sajjad Ali Haider, Nadia N. Qadri
2020 Human-Centric Computing and Information Sciences  
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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