| Function to obtain the best attribute subset on training samples. |
| Inputs: samples (a set of training samples), potAttr (set of potential attributes), algorithm (MLR, |
| M5′ or ANN), minYear, maxYear (minimum and maximum year in the training dataset). |
| resultList is a dynamic list and a global variable. Each entry in this list has the form <testAttr, |
| metricMeasures>, |
| where testAttr is an attribute subset and metricMeasures are the metrics results obtained from an |
| model’s evaluation made with attributescontained in testAttr |
| Function findBestAttrSubset(samples, potAttr, algorithm, minYear, maxYear) { |
| clearList(resultList) |
| localSamples = extract samples from samples in the range minYear, maxYear − 1 |
| validSamples = extract samples from samples with year equals to maxYear |
| for i = 0 to sizeOf(potentialAttr) begin |
| testAttr = create an empty set of attributes |
| testAttr = testAttr |
| // call a recursive procedure to evaluate all attribute subsets starting from the attribute in |
| potAttr |
| testAttrCombination(potAttr, testAttr, localSamples, validSamples, algorithm) |
| end_for |
| return the results at the top of resultList |
| end_function |
| |
| // Recursive procedure to evaluate an attribute combination |
| // Inputs: potAttr, testAttr (a set of attributes); trainSamples, validSamples (a set of samples), |
| algorithm (a regression algorithm). |
| procedure testAttrCombination(potAttr, testAttr, trainSamples, validSamples, algorithm) |
| Begin |
| // make a regression model of algorithm type using trainSamples and testAttr |
| model = makeRegressionModel(algorithm, trainSamples, testAttr) |
| // evaluate a regression model using validSamples |
| metricMeasures = evalModel(model, validSamples) |
| // add the attribute subset and the metric measures in the sorted result list |
| addResults(resultList, ) |
| index = obtain the highest position of one element of testAttr in potAttr |
| for i = index + 1 to length(potAttr) begin |
| // add the potential attribute to testAttr |
| testAttr = testAttr |
| // recursive call |
| testAttrCombination(potAttr, testAttr, trainSamples, validSamples, algorithm) |
| // remove the potential attribute i from testAttr |
| testAttr = testAttr − |
| end_for |
| end_procedure |
