hybrid-fuzzy-rough-rule-induction-and-feature-selection | Hybrid fuzzy-rough Rule induction
kandi X-RAY | hybrid-fuzzy-rough-rule-induction-and-feature-selection Summary
kandi X-RAY | hybrid-fuzzy-rough-rule-induction-and-feature-selection Summary
hybrid-fuzzy-rough-rule-induction-and-feature-selection is a Java library. hybrid-fuzzy-rough-rule-induction-and-feature-selection has no bugs, it has no vulnerabilities and it has low support. However hybrid-fuzzy-rough-rule-induction-and-feature-selection build file is not available. You can download it from GitHub.
This reposoirty is implementation of the paper Hybrid Fuzzy-Rough Rule Induction and Feature Selection 2009. This paper proposes an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective. In this project wine dataset is used which contains the results of a chemical analysis of wines grown in a specific area of Italy. Three types of wine are represented in the 178 samples, with the results of 13 chemical analyses recorded for each sample.
This reposoirty is implementation of the paper Hybrid Fuzzy-Rough Rule Induction and Feature Selection 2009. This paper proposes an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective. In this project wine dataset is used which contains the results of a chemical analysis of wines grown in a specific area of Italy. Three types of wine are represented in the 178 samples, with the results of 13 chemical analyses recorded for each sample.
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hybrid-fuzzy-rough-rule-induction-and-feature-selection has a low active ecosystem.
It has 1 star(s) with 1 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 0 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of hybrid-fuzzy-rough-rule-induction-and-feature-selection is current.
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hybrid-fuzzy-rough-rule-induction-and-feature-selection has no bugs reported.
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hybrid-fuzzy-rough-rule-induction-and-feature-selection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
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hybrid-fuzzy-rough-rule-induction-and-feature-selection does not have a standard license declared.
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hybrid-fuzzy-rough-rule-induction-and-feature-selection releases are not available. You will need to build from source code and install.
hybrid-fuzzy-rough-rule-induction-and-feature-selection has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are available. Examples and code snippets are not available.
Top functions reviewed by kandi - BETA
kandi has reviewed hybrid-fuzzy-rough-rule-induction-and-feature-selection and discovered the below as its top functions. This is intended to give you an instant insight into hybrid-fuzzy-rough-rule-induction-and-feature-selection implemented functionality, and help decide if they suit your requirements.
- Command - line parser
- Randomly shuffle the data set
- Performs a fast rule elimination
- Checks the induced rules
- Write rules to console
- Makes a FIS based on the feature rules
- Calculates the standard deviations
- Calculates the A - A point A
- Calculates the positive region of the B
- Read data set
- Calculate degree of dependence of B
- Randomly shuffle an array
- Calculates the biscernibility
- Computes the number of objects covered by the given sample
- Computes the positive region A
- Calculates the importance of a data point
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hybrid-fuzzy-rough-rule-induction-and-feature-selection Key Features
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This project uses java 8:.
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