Update README.md

README.md

@@ -10,13 +10,14 @@ Extracts meta-features from datasets to support the design of recommendation sys
 
 ## Measures
 
-In MtL, meta-features are designed to extract general properties able to characterize datasets. The meta-feature values should provide relevant evidences about the performance of algorithms, allowing the design of MtL-based recommendation systems. Thus, these measures must be able to predict, with a low computational cost, the performance of the  algorithms under evaluation. In this package, the meta-feature measures are divided into five groups:
+In MtL, meta-features are designed to extract general properties able to characterize datasets. The meta-feature values should provide relevant evidences about the performance of algorithms, allowing the design of MtL-based recommendation systems. Thus, these measures must be able to predict, with a low computational cost, the performance of the  algorithms under evaluation. In this package, the meta-feature measures are divided into six groups:
 
 * **General**: General information related to the dataset, also known as simple measures, such as number of instances, attributes and classes.
 * **Statistical**: Standard statistical measures to describe the numerical properties of a distribution of data.
 * **Information-theoretic**: Particularly appropriate to describe discrete (categorical) attributes and their relationship with the classes.
 * **Model-based**: Measures designed to extract characteristics like the depth, the shape and size of a Decision Tree (DT) model induced from a dataset.
 * **Landmarking**: Represents the performance of simple and efficient learning algorithms.
+* **Relative Landmarking**: Represents the relative landmarking meta-features with ranking strategy.
 
 ## Dependencies