kdd_coil_6

Author: Source: Unknown - Date unknown Please cite: %%%%%%%%%%%%%%%%%%% Data-Description % %%%%%%%%%%%%%%%%%%% COIL 1999 Competition Data Data Type multivariate Abstract This data set is from the 1999 Computational Intelligence and Learning (COIL) competition. The data contains measurements of river chemical concentrations and algae densities. Sources Original Owner [1]ERUDIT European Network for Fuzzy Logic and Uncertainty Modelling in Information Technology Donor Jens Strackeljan Technical University Clausthal Institute of Applied Mechanics Graupenstr. 3, 38678 Clausthal-Zellerfeld, Germany [2]tmjs@itm.tu-clausthal.de Date Donated: September 9, 1999 Data Characteristics This data comes from a water quality study where samples were taken from sites on different European rivers of a period of approximately one year. These samples were analyzed for various chemical substances including: nitrogen in the form of nitrates, nitrites and ammonia, phosphate, pH, oxygen, chloride. In parallel, algae samples were collected to determine the algae population distributions. Other Relevant Information The competition involved the prediction of algal frequency distributions on the basis of the measured concentrations of the chemical substances and the global information concerning the season when the sample was taken, the river size and its flow velocity. The competition [3]instructions contain additional information on the prediction task. Data Format There are a total of 340 examples each containing 17 values. The first 11 values of each data set are the season, the river size, the fluid velocity and 8 chemical concentrations which should be relevant for the algae population distribution. The last 8 values of each example are the distribution of different kinds of algae. These 8 kinds are only a very small part of the whole community, but for the competition we limited the number to 7. The value 0.0 means that the frequency is very low. The data set also contains some empty fields which are labeled with the string XXXXX. The training data are saved in the file: analysis.data (ASCII format). Table 1: Structure of the file analysis.data A K a g CC[1,1] CC[1,11] AG[1,1] AG[1,7] CC[200,1] CC[200,11] AG[200,1] AG[200,7] Explanation: CC[i,j]: Chemical concentration or river characteristic AG[i,j]: Algal frequency The chemical parameters are labeled as A, ..., K. The columns of the algaes are labeled as a, ..,g. Past Usage [4]The Third (1999) International COIL Competition Home Page _________________________________________________________________ [5]The UCI KDD Archive [6]Information and Computer Science [7]University of California, Irvine Irvine, CA 92697-3425 Last modified: October 13, 1999 References 1. http://www.erudit.de/ 2. mailto:tmjs@itm.tu-clausthal.de 3. file://localhost/research/ml/datasets/uci/raw/data/ucikdd/coil/instructions.txt 4. http://www.erudit.de/erudit/activities/ic-99/index.htm 5. http://kdd.ics.uci.edu/ 6. http://www.ics.uci.edu/ 7. http://www.uci.edu/ %%%%%%%%%%%%%%%%%%% Task-Description % %%%%%%%%%%%%%%%%%%% Third International Competition Protecting rivers and streams by monitoring chemical concentrations and algae communities. Intelligent Techniques for Monitoring Water Quality using chemical indicators and algae population Recent years have been characterised by increasing concern at the impact man is having on the environment. The impact on the environment of toxic waste, from a wide variety of manufacturing processes, is well known. More recently, however, it has become clear that the more subtle effects of nutrient level and chemical balance changes arising from farming land run-off and sewage water treatment also have a serious, but indirect, effect on the states of rivers, lakes and even the sea. In temperate climates across the world summers are characterized by numerous reports excessive summer algae growth resulting in poor water clarity, mass deaths of river fish from reduced oxygen levels and the closure of recreational water facilities on account of the toxic effects of this annual algal bloom. Reducing the impact of these man-made changes in river nutrient levels has stimulated much biological research with the aim of identifying the crucial chemical control variables for the biological processes. The data used in this problem comes from one such study. During the research study water quality samples were taken from sites on different European rivers of a period of approximately one year. These samples were analyzed for various chemical substances including: nitrogen in the form of nitrates, nitrites and ammonia, phosphate, pH, oxygen, chloride. In parallel, algae samples were collected to determine the algae population distributions. It is well known that the dynamics of the algae community is determined by external chemical environment with one or more factors being predominant. While the chemical analysis is cheap and easily automated, the biological part involves microscopic examination, requires trained manpower and is therefore both expensive and slow. Diatoms like Cymbella are major contributors to primary production throughout the world. The diatom reacts with large sensitivity to even small changes in acidity . Over a three and half billion year history algae have evolved and adapted as primary plant colonizers of almost every known habitant in terrestrial and aquatic environments. They respond very rapidly to man-made environment changes. The relationship between the chemical and biological features is complex and can be expected to need the application of advanced techniques. Typical of such real-life problems, the particular data set for the problem contains a mixture of (fuzzy) qualiative variables and numerical measurement values, with much of the data being incomplete. The competition task is the prediction of algal frequency distributions on the basis of the measured concentrations of the chemical substances and the global information concerning the season when the sample was taken, the river size and its flow velocity. The two last variables are given as linguistic variables. 340 data sets were taken and each contain 17 values. The first 11 values of each data set are the season, the river size, the fluid velocity and 8 chemical concentrations which should be relevant for the algae population distribution. The last 8 values of each data set are the distribution of different kinds of algae. These 8 kinds are only a very small part of the whole community, but for the competition we limited the number to 7. The value 0.0 means that the frequency is very low. The data set also contains some empty fields which are labeled with the string XXXXX. Each participant in the competition receives 200 complete data sets (training data) and 140 data sets (evaluation data) containing only the 11 values of the river descriptions and the chemical concentrations. This training data is to be used in obtainin a 'model' providing a prediction of the algal distributions associated with the evaluation data. The training data are saved in the file: analysis.txt (ASCII format). Structure of the file analysis.txt A K a g CC1,1 ... CC1,11 AG1,1 ... AG1,7 .... ... ... ... CC200,1 ... CC200,11 AG240,1 ... AG240,7 Explanation: CCi,j: Chemical concentration j=1,..11 AGi,k: Algal frequency k=1...7 The chemical parameters are labeled as A, ..., K. The columns of the algaes are labeled as a, ..,g. Evaluation data are saved in file eval.txt (ASCII format). Table 2: Structure of the file eval.* A K CC1,1 ... CC1,11 ..... ... CC140,1 ... CC140,11 _____________________________________________________________ Objective The objective of the competition is to provide a prediction model on basis of the training data. Having obtained this prediction model, each participant must provide the solution in the form of the results of applying this model to the evaluation data. The results obtained in this way should correspond to the results of the evaluation data (which are known to the organizer). The criteria used to evaluate the results is given below. All 7 Algae frequency distributions must be determined. For this purpose any number of partial models may be developed. _____________________________________________________________ Judgment of the results To judge the results, the sum of squared errors will be calculated. The following Table describes the results of a particular participant. Matrix of results a g Res1,1 ... Res1,7 .... ... Res140,1 Res140,7 All solutions that lead to a smallest total error will be regarded as winner of the contest. Information about the dataset CLASSTYPE: numeric CLASSINDEX: last ALGAE #: 6/7