coraAI: The Cora AI Data
In spa: Implements The Sequential Predictions Algorithm

Description Usage Format Details Source References

The coraAI data consists of a response, journal indication matrix, and co-citation network. This data is a subset of the Cora text mining project (refer to reference).

The observations are text documents that consist of 879 published papers about either Artificial Intelligence (AI) or Machine Learning (ML). The journal name for each document is available (8 journals and an other category). The observed co-citation graph is also available, where each vertex is a document (observation), and the edge is the count of citations in common between each document and all other documents.

The goal is to incorporate both the text information and co-citation information for the prediction of paper subject AI/ML. Another, interesting problem might be to predict the journal of the paper given the text information and the categorization.

1	data(coraAI)

The coraAI data consists of three objects each discussed next.

class: categorization of the document(observation) as either AI or ML. Typically the response.

journals: indication of the document as published in a specific journal, (other, artificial-intelligence, machine-learning, nueral-computing, ieee-trans-Nnet, ieee-tpami, j-artificial-intelligence-research, ai-magazine, JASA)

cite: the adjacency matrix of the co-citation network for these 879 documents.

The spa is particularly appealing for this data since it fits a function directly to the graph and coeficient vector to the journals. Other approaches require convergence of the journal information into a graph for processing, which is unclear when the data is a binary design matrix.

The data was generated using AWK scripting from the cora raw sweet (first reference). The journal names were fixed to obtain a useable representation (e.g. tpami, ieee tpami, pami are all ieee-tpami).

A. McCallum, K. Nigam, J. Rennie, and K. Seymore (2000). Automating the construction of internet portals with machine learning. Information Retrieval Journal, 3.

M. Culp (2011). spa: A Semi-Supervised R Package for Semi-Parametric Graph-Based Estimation. Journal of Statistical Software, 40(10), 1-29. URL http://www.jstatsoft.org/v40/i10/.

spa documentation built on May 30, 2017, 1:27 a.m.