Introduction to "Machine learning" wekaの Feature Selection

Read this blog should understand that the feature selection code implementation should consist of 3 parts:

1. Search algorithm;
2. evaluation function;
3. Data

Therefore, the general form of the code is:

attributeselection Attsel = new Attributeselection ();//Create and initiate a new Attributeselection instance
Ranker search = new Ranker ();//Choose a search Method
principalcomponents eval = new principalcomponents ();//Choose an evaluation method
Attsel.setevaluator (eval);//Set Evaluation method
Attsel.setsearch (search);//Set Search method
Attsel. Selectattributes (data); Set the data to is used for attribute selection

Where the search method and the evaluation function are different:

Property Evaluation methods:

Cfssubseteval: Evaluates the ability to predict each feature in a subset of attributes and the correlation between them.

Gainratioattributeeval: evaluated based on the gain ratio of each attribute associated with the classification.

Infogainattributeeval: evaluated based on the information gain of each attribute associated with the classification.

Chisquaredattributeeval: evaluated based on the Chi-square value of each attribute associated with the classification.

Symmetricaluncertatrributeeval: evaluated based on the symmetry instability of each attribute associated with the classification.

Classifiersubseteval: Evaluates a subset of attributes based on data other than the training set or test set.

Consistencysubseteval: To evaluate the consistency of the classification values obtained by using the subset of attributes.

Costsensitiveattributeeval: Depending on how the base subset evaluates the cost sensitivity, the variance selects the subset evaluation method.

Costsentitivesubseteval: The same way.

Filteresattributeeval: Any property evaluation that runs on data after any filter.

Filteredsubseteval: The same way.

Latensemanticanalysis: evaluated based on the potential semantic analysis and transformation of the data, combined with random search.

Onerattributeeval: Evaluates attributes based on the Oner classifier.

Principalcomponents: evaluated based on the main component analysis and conversion of the data.

Relieffattributeeval: Evaluated by repeatedly testing an instance and the attribute values on the nearest instance of its homogeneous or non-homogeneous class.

Significanceattributeeval: Calculates the value of the probabilistic meaning of the bidirectional function evaluation attribute.

Symmetricaluncertatrributeseteval: evaluated based on the symmetric instability of each property associated with the other attribute set.

Wrappersubseteval: An attribute set is evaluated using a learning pattern.

Search algorithm:

Bestfirst: Traceable Greedy search expansion, the best priority principle.

Exhaustivesearch: Exhaustive search, starting from the empty set.

Fcbfsearch: Feature Selection method based on correlation analysis. Relevance matching search.

Geneticsearch:goldberg (1989) presents a simple genetic algorithm.

Greedystepwise: A single step forward or backward search.

Linearforwardselection: Linear forward search.

Racesearch: A cross-validation error condition that compares a subset of features.

Randomsearch: Random Search.

Ranker: Sorts the attribute values.

Ranksearch: Select an evaluator to sort the attributes.

ScatterSearchV1: Discrete search.

Subsetsizeforwardselection: Forward linear search by feature subset size, which is an extension of linear search.

Tabusearch: Taboo Search.

subset Search Methods:
1. Bestfirst
2. Greedystepwise
3. Fcbfsearch (ASU)

subset Evaluation Methods:
1. Cfssubseteval
2. Symmetricaluncertattributeseteval (ASU)

individual Search Methods:
1. Ranker

individual Evaluation Methods:
1. Correlationattributeeval
2. Gainratioattributeeval
3. Infogainattributeeval
4. Onerattributeeval
5. Principalcomponents (used with a rander search to perform PCA and data transform
6. Relieffattributeeval
7. Symmetricaluncertattributeeval

Code styles can be consulted: http://java-ml.sourceforge.net/content/feature-subset-selection

Introduction to "Machine learning" wekaの Feature Selection