Feature Selection Benjamin Biesinger - Manuel Maly - Patrick Zwickl

Agenda Introduction: What is feature selection? What is our contribution? Phases: What is the sequence of actions in our solution? Solution: How does it work in particular? Results: What is returned? Analysis: What to do with it? What can we conclude from it?

Introduction Not all features of a data set are useful for classification A large number of attributes negatively influences the computation time The most essential features should be used for classification Feature selection is an approach Different search strategies and evaluations are available, but which is the best? Automatic feature selection: Several algorithms are run, compared and analyzed for trends → Implemented by us

Phases Phases: (I) Meta-classification - (II) Classification Before: File loading & preparation Afterwards: Comparison + output generation

Solution Java command-line application utilizing the WEKA toolkit Command-line arguments: Filename (of dataset), Classifier algorithm name, Split (feature selection classification percentage) Example: „winequality-red.csv M5Rules 20“ Computation of results and display in system output of console

Solution (Flow 1) 1. Parsing of dataset and creation of WEKA-specific „Instances“ object. 2. Split of Instances object in two parts, depending on percentage entered by user. 3. Combining all evaluation and search algorithms given in properties-files, and applying on 1. Instances object, finally storing results in dedicated objects (SData). 4. Classifying all combinations from step 3 with classifier entered by user on 2. Instances object. Again storing results in SData objects.

Solution (Flow 2) 5. Gaining aggregate information on all results by iterating over SData objects. 6. Print trend analysis and information on combined evaluation and search algorithms, plus the corresponding classification results (time + mean absolute error).

Solution (Output of selected features Attribute: bottom-right-square has Count: 8 … =============== Evaluation: ConsistencySubsetEval =============== --- Search: GreedyStepwise --- # of selected features: 1, selection time: 34, classification time: 36, mean abs. error:47,07% # of selected features: 2, selection time: 35, classification time: 34, mean abs. error:43,16% … --- Search: RandomSearch --- Automatic feature number (no influence by user): 5, selection time: 74, classification time: 118, mean abs. error:44,46%

Results Tested on 3 different datasets Tic Tac Toe Wine Quality (red) Balance Scale 2 comparisons per dataset were made For each feature selection individually Between different feature selection techniques Is there a trend which features are selected by most techniques?

1st Comparison Influence of number of selected features on Runtime Classification accuracy (measured in MAE)

1st Comparison Result Only those search algorithms used that implement RankedOutputSearch interface Capable to influence the number of features to select Number of features selected and MAE behave to each other directly proportional – to runtime inversely proportional

2nd Comparison Feature Selection Technique consists of Search algorithm Evaluation algorithm Not all combinations possible! Different feature selection techniques compared to each other concerning: Runtime Performance (measured in MAE)

2nd Comparison Result Different techniques select different amount of attributes In some extent, different attributes, too Some techniques are slower than others Huge runtime differences between search algorithms Some techniques select insufficient attributes to give acceptable results

Trend In all tested datasets there was a trend on which features were selected Higher count of selection implies bigger influence to the output

Analysis Different feature selection techniques – different characteristics ClassifierSubsetEval / RaceSearch very good classification results Less attributes – faster classification Algorithms that select less features are faster e.g. GeneticSearch

Lowest error rate Dataset Feature Selection Technique Runtime Mean absolute error Tic Tac Toe ClassifierSubsetEval / RaceSearch 64215,25 Wine Quality (red) ClassifierSubsetEval / RaceSearch ,8 Balance Scalemany9-3421,96

Lowest runtime Dataset Feature Selection Technique Runtime Mean absolute error Tic Tac Toex / RankSearch1750,85 Wine Quality (red) WrapperSubsetEval / GeneticSearch ,57 Balance Scalemany5-34-

Trend DatasetFirstSecondThird Tic Tac ToeTop-left-squareTop-right-squareTop-middle-square Wine Quality (red) Volatile acidityFixed acidityChlorides Balance ScaleRight-weightRight-distanceLeft-distance

Feature Selection Benjamin Biesinger - Manuel Maly - Patrick Zwickl Any questions? The essential features ;) hääh? Anything missed? thx