Characterization of the Row Geometric Mean Ranking with a Group Consensus Axiom

An axiomatic approach is applied to the problem of extracting a ranking of the alternatives from a pairwise comparison ratio matrix. The ordering induced by row geometric mean method is proved to be uniquely determined by three independent axioms, anonymity (independence of the labelling of alternatives), responsiveness (a kind of monotonicity property) and aggregation invariance, which requires the preservation of group consensus, that is, the pairwise ranking between two alternatives should remain unchanged if unanimous individual preferences are combined by geometric mean.Comment: 17 pages, 2 figure

Data from: Predicting the maximum earthquake magnitude from seismic data in Israel and its neighboring countries

This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data was obtained from the Geophysical Institute of Israel. Each earthquake record in the catalog is associated with one of 33 seismic regions. The data was cleaned by removing foreshocks and aftershocks. In our study, we have focused on ten most active regions, which account for more than 80% of the total number of earthquakes in the area. The goal is to predict whether the maximum earthquake magnitude in the following year will exceed the median of maximum yearly magnitudes in the same region. Since the analyzed catalog includes only 28 years of complete data, the last five annual records of each region (referring to the years 2006–2010) are kept for testing while using the previous annual records for training. The predictive features are based on the Gutenberg-Richter Ratio as well as on some new seismic indicators based on the moving averages of the number of earthquakes in each area. The new predictive features prove to be much more useful than the indicators traditionally used in the earthquake prediction literature. The most accurate result (AUC = 0.698) is reached by the Multi-Objective Info-Fuzzy Network (M-IFN) algorithm, which takes into account the association between two target variables: the number of earthquakes and the maximum earthquake magnitude during the same year

Information Network (IN) and Multi-Objective Info-Fuzzy Network (M-IFN) Software

IN and M-IFN Software (V. 1.90 .NET Framework 2.0). Developed by Mark Last. Further information is available in the ReadMe file

The M-IFN ROC Curve

<p>The M-IFN ROC Curve</p

Preprocessed yearly records of seismic events

Training data file: Top10-all-win2-train.csv (136 records related to years 1988-2005). Testing data file: Top10-all-win2-test.csv (49 records related to years 2006-2010). The data includes only top 10 areas having the highest number of earthquakes. The list of top 10 areas is shown in Table 1 of the paper. The format of all files in the folder is compatible with the IN / M-IFN software