Feature SelectionBenjamin 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 Excerpt)

@TREND 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

DatasetFeature Selection

TechniqueRuntime

Mean absolute error

Tic Tac ToeClassifierSubsetEval /

RaceSearch642 15,25

Wine Quality (red)

ClassifierSubsetEval / RaceSearch

3594 50,8

Balance Scale

many 9-34 21,96

Lowest runtime

DatasetFeature Selection

TechniqueRuntime

Mean absolute error

Tic Tac Toe x / RankSearch 17 50,85

Wine Quality (red)

WrapperSubsetEval / GeneticSearch

1732 63,57

Balance Scale

many 5-34 -

Trend

Dataset First Second Third

Tic Tac Toe Top-left-squareTop-right-

squareTop-middle-square

Wine Quality (red)

Volatile acidity Fixed acidity Chlorides

Balance Scale

Right-weight Right-distance Left-distance

Feature SelectionBenjamin Biesinger - Manuel Maly - Patrick Zwickl

Any Any questions?questions? The essential features

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AnythinAnything g

missed?missed?

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