New Metafeatures

[emrysshevek]

This is a list of metafeatures to be implemented for metafeature experiments and other use. This list will be updated with more specific metafeatures as more research is performed.

Metafeatures

• [ ] Radius Neighbors Graph based:
  • [x] Number of Nodes (not really necessary since it's just the number of instances)
  • [ ] Number of Edges
  • [ ] (un)weighted diameter (longest shortest path in the graph)
  • [ ] (un)weighted shortest paths (over all pairs of nodes)
  • [ ] clustering (fraction of a vertices neighbors that are also neighbors of each other)
  • [ ] degree (number of vertices adjacent to a vertex)
  • [ ] class change ratio (number of edges connecting vertices of different classes)
• [ ] More Model-based:
  • [x] KNN (no longer going to implement)
  • [ ] Perceptron ( on full dataset, 1/10 dataset, 1/2 dataset, sqrt dataset)
  • [ ] sum of weights
  • [ ] distribution of weights
  • [ ] distribution of biases
  • [ ] number of iterations to convergance
  • [ ] Clustering
  • [ ] Bayesian Network
• [ ] Timing
• [ ] DCoL
  • [ ] Regression
  • [ ] Classification
• [ ] Time-series

Summarization

• [ ] Meta-metafeatures
• [ ] sum

[MichaelMMeskhi]

Regarding DCoL, from what I have experienced, having it wrapped in Python would be best. Topological calculations are slow thus keeping them in C++ might be best. I have done some of the DCoL wrapping last year for personal purposes might give it a try again for this project if I find time.

[emrysshevek]

That would be great if you have time! Which parts have you done so far?

[MichaelMMeskhi]

I've personally wrapped computeNonLinearityLCDistance just for something specific I was trying to do. But I will look back at DCoL and see what to do with it. I was thinking of maybe even using Cython? Not sure how that will affect performance yet. Doing higher level math in C++ is way faster.

[emrysshevek]

I'm not really sure how the DCoL package works, is there a quick/easy way to just wrap a call to get all of the metrics for a given dataset?

[MichaelMMeskhi]

There is a way but if I remember correctly it writes the metrics to a file. Some modifications must be made in C++ before wrapping.

[MichaelMMeskhi]

This is a list of metafeatures to be implemented for metafeature experiments and other use. This list will be updated with more specific metafeatures as more research is performed.

Metafeatures

• [ ] Radius Neighbors Graph based:

  • [x] Number of Nodes (not really necessary since it's just the number of instances)
  • [ ] Number of Edges
  • [ ] (un)weighted diameter (longest shortest path in the graph)
  • [ ] (un)weighted shortest paths (over all pairs of nodes)
  • [ ] clustering (fraction of a vertices neighbors that are also neighbors of each other)
  • [ ] degree (number of vertices adjacent to a vertex)
  • [ ] class change ratio (number of edges connecting vertices of different classes)

• [ ] More Model-based:

  • [ ] KNN

  • [ ] Perceptron

  • [ ] Perceptron sum of weights on full dataset, 1/10 dataset, 1/2 dataset, sqrt dataset

  • [ ] Clustering

  • [ ] Bayesian Network

• [ ] Regression

• [ ] Timing

• [ ] DCoL

• [ ] Measures of overlaps in the feature values from different classes.

• [ ] The maximum Fisher's discriminant ratio (F1).

• [ ] The directional-vector maximum Fisher's discriminant ratio (F1v).

• [ ] The overlap of the per-class bounding boxes (F2).

• [ ] The maximum (individual) feature efficiency (F3).

• [ ] The collective feature efficiency (F4).

• [ ] Measures of class separability.

• [ ] The leave-one-out error rate of the one-nearest neighbor classifier (L1).

• [ ] The minimized sum of the error distance of a linear classifier (L2).

• [ ] The fraction of points on the class boundary (N1).

• [ ] he ratio of average intra/inter class nearest neighbor distance (N2).

• [ ] The training error of a linear classifier (N3).

• [ ] Measures of geometry, topology, and density of manifolds.

• [ ] The nonlinearity of a linear classifier (L3).

• [ ] The fraction of maximum covering spheres (T1).

• [ ] The average number of points per dimension (T2).

• [ ] Time-series

Summarization

• [ ] Meta-metafeatures
• [ ] sum

[MichaelMMeskhi]

Suggestion to add a new meta-feature: Concept Variation (Task complexity) as defined here.