Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Feature Weighting Problem in k-Nearest Neighbor Classifier and Evolutionary Solution Approach
Date
2017-10-21
Author
İyigün, Cem
Metadata
Show full item record
Item Usage Stats
38
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/82830
Collections
Unverified, Conference / Seminar
Suggestions
OpenMETU
Core
Feature weighting problem in k-Nearest neighbor classifier
Güleç, Nurullah; İyigün, Cem; Department of Operational Research (2017)
The k-Nearest Neighbor (k-NN) algorithm is one of the well-known and most common used algorithms for the classification problems. In this study, we have focused on feature weighted k-NN problems. Two different problems are studied. In the first problem, k value and the weights of each feature are optimized to maximize the classification accuracy. Objective function of the problem is nonconvex and nonsmooth. As a solution approach, Forest Optimization Algorithm (FOA), which is a newly introduced evolutionary...
Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning
Ertekin Bolelli, Şeyda (2021-06-09)
Data annotation for training of supervised learning algorithms has been a very costly procedure. The aim of deep active learning methodologies is to acquire the highest performance in supervised deep learning models by annotating as few data points as possible. As the feature space of data grows, the application of linear models in active learning settings has become insufficient. Therefore, Deep Bayesian Active Learning methodology which represents model uncertainty has been widely studied. In this paper, ...
Feature extraction from acoustic and hyperspectral data by 2d local discriminant bases search
Kalkan, Habil; Kalkan, Habil; Department of Information Systems (2008)
In this thesis, a feature extraction algorithm based on 2D Local Discriminant Bases (LDB) search is developed for acoustic and hyperspectral data. The developed algorithm extracts the relevant features by both eliminating the irrelevant ones and/or by merging the ones that do not provide extra information on their own. It is implemented on real world data to separate aflatoxin contaminated or high risk hazelnuts from the sound ones by using impact acoustic and hyperspectral data. Impact acoustics data is us...
FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION
Ozkan, Savas; Ates, Tayfun; Tola, Engin; Soysal, Medeni; Esen, Ersin (2014-04-25)
In this work, we survey the perormance of various feature encoding models for geographic image retrieval task Recently introduced Vector-of-Locally-Aggregated Descriptors (VLAD) and its Product Quantization encoded binary version VLAD-PQ are compared with the widely used Bag-of-Word (BoW) model. Evaluation results are shown on a publicly available 21-class LULC dataset. With experiments, it is shown that VLAD outperforms classical BoW representation albeit with some increases in the computation time. Additi...
Feature extraction of hidden oscillation in ECG data via multiple-FOD method
Purutçuoğlu Gazi, Vilda; Erkuş, Ekin Can (null; 2019-10-30)
Fourier transform (FT) is a non-parametric method which can be used to convert the time domain data into the frequency domain and can be used to find the periodicity of oscillations in time series datasets. In order to detect periodic-like outliers in time series data, a novel and promising method, named as the outlier detection via Fourier transform (FOD), has been developed. From our previous studies, it has been shown that FOD outperforms most of the commonly used approaches for the detection of outliers...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
C. İyigün, “Feature Weighting Problem in k-Nearest Neighbor Classifier and Evolutionary Solution Approach,” 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/82830.