Index:Software
What is Coser?
  • 1. Coser (COst-SEnsitive Rough sets) is a software dedicated to rough sets problems, especially those related to cost-sensitive learning.
  • 2. Coser has a good graphics user interface (GUI), and in each dialog there is a [Help] button to provide information of parameters, source code file names, and related papers.
  • 3. Coser is a green software. You can download it, unzip it, and run it immediately.
  • 4. Coser is an open source software written in Java. The source code is well documented, and standard Java help files (in .html format) are generated.
  • 5. Coser is an evolving software. We are updating it frequently to include more algorithms. 
Who uses Coser?
  • 1. We use Coser to undertake experiments on our algorithms and compare them with algorithms in related works.
  • 2. Reviewers of our papers may use Coser to check the effectiveness of our algorithms.
  • 3. You can use Coser to implement your algorithms and compare them with ours!
What is the platform requirement of Coser?
  • 1. Windows. Coser has not been tested on other operating systems yet. However, since Coser is written in Java, it should be easily migrated.
  • 2. JDK or JRE 1.5 or higher. In most cases you have it already.
  • 3. Weka. This is because that we use some APIs in Weka.
What is the platform requirement of Coser?
How to repeat our experiments?
  • If you are a reviewer or a reader of our papers, please follow the instruction below to obtain our results. Note the numbers are the same as my publication
76. Fan Min, Qinghua Hu and William Zhu, Granular association rules on two universe with four measures (submitted to Information Sciences).
  • 76.1 Start coser by double clicking coser.bat
  • 76.2 Grarule -> Load MMER -> (choose three arff file, examples are already there) OK
  • 76.3 Grarule -> Rule generation -> (settings as indicated in the paper) -> Compute. Rules are given in the dialog, and run time information are given in the console.
75. Fan Min, Qinghua Hu and William Zhu, Feature selection with test cost constraint (submitted to International Journal of Approximate Reasoning).
  • 75.1 Start coser by double clicking coser.bat
  • 75.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value) OK
  • 75.3 TCS-DS -> Test-cost constraint reduction (exhaustive) -> settings: algorithm = ALL, consistency metric = POS, number of experiments = 100 -> Compute. See "[6]: the time for the execution" for SESRA, SESRA* and Backtrack
  • 75.4 TCS-DS -> Constraint reduction (compared with optimal) -> settings: heuristic mode = information entropy, lambda upper bound = 0, lambda lower bound = -3, number of experiments = 1000 -> Compute. Only observe the "Finding optimal factor" and "Average run time."
74. Fan Min and William Zhu, Attribute reduction on data with error ranges and test costs Information Sciences, vol. 211, pp. 48-67, November 2012.
  • 74.1 Start coser by double clicking coser.bat
  • 74.2 DS -> Load DS -> (choose a file, any data) OK
  • 74.3 DS -> Normalization -> OK (please remember the name of the normalized data)
  • 74.4 TCS-DS-ER -> Load TCS-DS-ER (specify the normalized filename) OK
  • 74.5 TCS-DS-ER -> Lambda weighted reduction (to obtain data for the heuristic algorithm)
  • 74.6 TCS-DS-ER -> Time comparison (to obtain data for the backtrack algorithm)
72. Fan Min and William Zhu, A competition strategy to cost-sensitive decision trees, RSKT, pp. 359-368, 2012.
  • 72.1 Start coser by double clicking coser.bat
  • 72.2 BCS-DS -> Load BCS-DS -> OK
  • 72.3 BCS-DS -> CC-DT parameter comparison -> choose different benchmark algorithms -> number of experiments = 1000 -> OK (three different algorithms correspond to two figures and one tables in the paper)
  • 72.4 BCS-DS -> CC-DT prune comparison -> number of experiments = 1000 -> OK
65. Fan Min, Huaping He, Yuhua Qian and William Zhu, Test-cost-sensitive attribute reduction, Information Sciences, vol. 181, Issue 22, pp. 4928-4942, November 2011.
  • 65.1 Start coser by double clicking coser.bat
  • 65.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value) OK
  • 65.3 TCS-DS -> Minimal test cost reduction -> compute
64. Fan Min and William Zhu, Attribute reduction with test cost constraint, Journal of Electronic Science and Technology, vol. 9, no. 2, pp. 97-102, June 2011.
  • 64.1 Start coser by double clicking coser.bat
  • 64.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value) OK
  • 64.3 TCS-DS -> Test cost constraint reduction -> compute
62. Fan Min and William Zhu, Minimal cost attribute reduction through backtracking, FGIT-DTA/BSBT, CCIS 258, pp. 100-107, 2011.
  • 62.1 Start coser by double clicking coser.bat
  • 62.2 BCS-DS -> Load BCS-DS -> (choose an arff file, nominal data without missing value, if the number of classes is more than 2, specify the misclassification matrix accordingly)
  • 62.3 BCS-DS -> Optimal reducts (backtrack) -> Compute
61. Fan Min and William Zhu, Optimal partial reducts with test cost constraint, RSKT, pp. 57-62, 2011.
  • 61.1 Start coser by double clicking coser.bat
  • 61.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value) OK
  • 61.3 TCS-DS -> Test cost constraint reduction (exhaustive) -> compute (choose algorithms SESRA and SESRA*)
60. Fan Min and William Zhu, Optimal sub-reducts in the dynamic environment, GrC, pp. 457-462, 2011.
  • 60.1 Start coser by double clicking coser.bat
  • 60.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value) OK
  • 60.3 TCS-DS -> Test cost constraint reduction (exhaustive) -> compute (choose algorithms BASS and ALL)
58. Hong Zhao and Fan Min, Test-cost-sensitive attribute reduction based on neighborhood Rough set, GrC, pp. 802-806, 2011.
  • 58.1 Start coser by double clicking coser.bat
  • 58.2 DS -> Load DS -> (choose an arff file, any data) OK
  • 58.3 DS -> Normalization -> OK (please remember the name of the normalized data)
  • 58.4 TCS-DS-NH -> Load TCS-DS-NH -> (specify the normalized filename) OK
  • 58.5 TCS-DS-NH -> Test cost neighborhood reduction
57. Guiying Pan, Fan Min and William Zhu, Test cost constraint reduction with common cost, FGIT, LNCS 7105, pp. 55-63, 2011.
  • 57.1 Start coser by double clicking coser.bat
  • 57.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value, the test cost relationship should be "Simple common")
  • 57.3 TCS-DS -> Simple common test cost constraint reduction -> Compute
56. Guiying Pan, Fan Min, Zhongmei Zhou, and William Zhu, A genetic algorithm to the minimal test cost reduct problem, GrC, pp. 539-544, 2011.
  • 56.1 Start coser by double clicking coser.bat
  • 56.2 TCS-DS -> Load TCS-DS -> (choose an arff file, nominal data without missing value) OK
  • 56.3 TCS-DS -> Minimal test cost constraint reduction based on GA -> compute
  • Copy results from the dialog to Excel and draw the lines immediately!
  • Of course, you can change data sets and settings to obtain your own results.
  • Attention: If you set a big "number of expereiments" (e.g., 4000), please be patient. You can view the process in the console. In most cases there is a display for every 50 experiements.
  • Good Luck!
  • This free software is developed by the Lab of Machine Learning, Southwest Petroleum University. If you use this software for you research works, please cite it as:
  • Fan Min, William Zhu, Hong Zhao, Guiying Pan, Coser: Coser-senstive rough set models, http://www.fansmale.com/software.html
  • For any questions and suggestions, please contact Fan Min minfanphd@163.com.
Last updated: June 5, 2017.