Correction: Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review
نویسندگان
چکیده
منابع مشابه
Feature Selection: A literature Review
Relevant feature identification has become an essential task to apply data mining algorithms effectively in real-world scenarios. Therefore, many feature selection methods have been proposed to obtain the relevant feature or feature subsets in the literature to achieve their objectives of classification and clustering. This paper introduces the concepts of feature relevance, general procedures,...
متن کاملFeature Selection Algorithms: Literature Review
Feature selection is a term usually use in data mining to demonstrate the tools and techniques available for reducing inputs to a convenient size for processing and analysis. In this paper authors has reviewed the literature of feature selection algorithms such as well known attributes selection methods of FCBF, ReliefF, SVM-RFE, Random selection. This review of literature focuses on how featur...
متن کاملA systematic review on supplier selection and order allocation problems
The supplier selection and order allocation are two key strategic decisions in purchasing problem. The review presented in this paper focuses on the supplier selection problems (SSP) and order allocation from year 2000 to 2017 in which a new structure and classification of the existing research streams and the different MCDM methods and mathematical models used for SSP will be presented. The re...
متن کاملAdaptive Feature Selection for Hyperspectral Data Analysis
Hyperspectral data can potentially provide greatly improved capability for discrimination between many land cover types, but new methods are required to process these data and extract the required information. Data sets are extremely large, and the data are not well distributed across these high dimensional spaces. The increased number and resolution of spectral bands, many of which are highly ...
متن کاملMargin-based feature selection for hyperspectral data
A margin based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin based evaluation functions, were used in the present study. Evaluation functions use linear, zero-one and sigmoid utility...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Food Measurement and Characterization
سال: 2023
ISSN: ['2193-4126', '2193-4134']
DOI: https://doi.org/10.1007/s11694-023-02148-4