A Soft-Computing Approach to Knowledge Flow Synthesis and Optimization
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1 A Soft-Computing Approach to Knowledge Flow Synthesis and Optimization Tomáš Řehořek Pavel Kordík Computational Intelligence Group (CIG), Faculty of Information Technology (FIT), Czech Technical University (CTU) in Prague September 5, 2012 T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
2 Introduction: Knowledge Flows in Predictive Analysis Tasks in Predictive Analysis Common tasks in Predictive Data Analysis: 1 Data Preprocessing Feature Selection, Transformation of Space (e.g. PCA) Methods can be put into a Chain 2 Selection Selection of Algorithms k-nn, Naive Bayes, Decision Tree, Neural Networks,..., Ensemble Techniques (Majority Vote, Bagging, Stacking... ) Parameter Tuning Distance Measure and k in k-nn, Minimal Gain for Split in Decision Tree T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
3 Introduction: Knowledge Flows in Predictive Analysis Dependence on Data The optimal choice of Preprocessing and ing methods is -dependent: Different Datasets require different preprocessing and are suitable for different modeling algorithms Traditional approach: Explore the in trial-and-error manner Even mining experts follow this scenario T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
4 Introduction: Knowledge Flows in Predictive Analysis Knowledge Flows Modern approach to express the whole process: Knowledge Flows (KFs) Directed Graphs of interconnected, properly configured Actions Example: KFs in RapidMiner 5 learning environment: T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
5 Optimization of Knowledge Flows Evolving Good Graphs Our Approach: KFs are viewed as Directed Acyclic Graphs (DAGs) with labeled nodes. Knowledge Discovery Workflow Process Labeled Directed Acyclic Graph R 1 d1 L m P d A d S d W R 2 d2 These graphs are subject to optimization by means of evolutionary computation. T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
6 Problem Statement: Finding Optimal Predictive Analysis KF for a given Dataset Given a Dataset D of examples from R n L, find: 1 Sequence p 1,..., p k of properly configured preprocessing actions, 2 Properly configured learning algorithm (or possible hierarchical ensemble of such algorithms) m such that model obtained as m (p k (... p 1 (D)...)) has minimal generalization error. Example: Feature Selection PCA Majority Vote (Decision Tree, k-nn, Naive Bayes) Dataset p 1 p 2 m Dataset T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
7 Methodology Embryonic STGP [Koza1997] We use Embryonic Strongly Typed Genetic Programming Proposed by J.R.Koza for evolving Analog Electrical Circuits [Koza1997] Encodes the graph in form of rooted tree The tree codes a plan for developing a complete graph from a simple graph referred to as the embryo Fitness function: classification accuracy 1 T (x,l) T { 0, m(x) l 1, m(x) = l T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
8 Methodology Embryonic KF in RapidMiner 5 We use the RapidMiner 5 software to measure fitness Embryonic Knowledge Flow for our experiment: Preprocessing Cross-Validation Preprocessing Dataset Proprocessing Learning Classification Testing Classification Accuracy Process Body Fixed Variable T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
9 Methodology STGP Grammar: Validaton Process Validation Process <ValidationProcess> ::= Process(<Preprocessing>,<Learner>) Preprocessing Learner Syntax Insert arbitrary number of preprocessing steps Substitute learner 20-fold Cross Validation Testing Semantics Dataset Training Learner Learning subprocess application Performace evaluation Classification accuracy Classification accuracy Testing subprocess T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow SynthesisSeptember and Optimization 5, / 15
10 application Performace evaluation Testing Classification accuracy application Performace evaluation Testing Classification accuracy application Performace evaluation Testing Classification accuracy Methodology STGP Grammar: Preprocessing, ing Learner <Learner> ::= <KNN> <NaiveBayes> <DecisionTree> <MajorityVote> <Bagging> K-NN Naive Bayes Decision Tree Majority Vote Bagging Syntax Preprocessing <Preprocessing> ::= <PCA> <FilterAttributes> PreprocessingTerminal K-NN Use k-nearest neighbor learner 20-fold Cross Validation Select PCA Projection Attributes <PCA> ::= PCA(<NaturalNumber>),<Preprocessing> PCA Projection Preprocessing Terminal Syntax Semantics Insert Principal Component Analysis (PCA) Dimensionality Reduction Natural Number Boolean constant Distance Measure Training Distance measure used? Weighted vote? K-NN Number of neighbors? Learning subprocess Testing subprocess Preprocessing sequence so far (potentially empty) PCA Naive Bayes Use Naive Bayes Learner 20-fold Cross Validation Natural Number Preprocessing <SelectAttributes> ::= SelectAttributes( <SetOfPositiveIntegers>, BooleanConstant), <Preprocessing>; Select Attributes Insert next preprocessing Set number of components here Insert Feature Selection Dimensionality Redution Preprocessing sequence so far (potentially empty) Select Attributes criterion Decision Tree Criterion Boolean constant Decision Tree pre-pruning Boolean constant Training Laplace Correlation? Use Decision Tree Learner Training Naive Bayes Learning subprocess 20-fold Cross Validation Naive Bayes Testing subprocess Set of Positive Integers Boolean constant Preprocessing Invert the selection? Insert next preprocessing here minimal size for split Natural Number pruning Boolean constant Apply parameters Learning subprocess Testing subprocess Set indices of attributes to be selected minimal leaf size Natural Number prepruning alternatives Real Number from [0, ] Preprocessing Terminal Do not insert any node Preprocessing sequence so far (potentially empty) minimal gain Real Number from [0, ] maximal depth Natural Size Bound confidence level Real Number from [1e-7, 0.5] Semantics T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow Synthesis September and Optimization 5, / 15
11 Preliminary Results Ecoli Dataset: Sample Tree Evolved Sample tree evolved on the Ecoli tset Attribute Selection (remove attribute #0) SA VP BG Naive Bayes Learner Bagging AS true PCA NB 0 3 EPS PCA Projection (3 components) Preprocessing Sequence Terminal T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow Synthesis September and Optimization 5, / 15
12 Preliminary Results Ecoli Dataset: Evolved KF in RapidMiner 5 T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow Synthesis September and Optimization 5, / 15
13 Preliminary Results Ecoli Dataset: Generation vs. Average Fitness of the Population 0.9 Ecoli Dataset: Generation vs. Average Fitness of the Population Average Fitness Generation Ecoli Dataset: Generation vs. Best Known Fitness T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow Synthesis September and Optimization 5, / 15
14 EPS VP NB true VM MSH NB true KNN 10 EuclideanDistance DTC 7 sqrt 8 abs NB NTB 2 * DT ^2 ^2 ^ * CNL * true DTC sqrt 9 10 abs DT NTB 5 CNL abs * 9 9 sqrt 2 KNN DTC CamberraDistance sqrt 9 8 abs NB NTB 5 DT CNL sqrt abs * KNN 8 8 CamberraDistance DTC * sqrt 5 5 NB 9 abs NB true NTB 3 DT CNL ^ DTC abs abs DT NTB 8 MST CNL abs 7 true Preliminary Results Very Complex Tree Evolved of the Vehicle Dataset A very complex tree evolved on the Vehicle set T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow Synthesis September and Optimization 5, / 15
15 Questions & Discussion Thank you for you attention! Tomáš Řehořek T. Řehořek, P. Kordík (FIT CTU Prague) A Soft-Computing Approach to Knowledge Flow Synthesis September and Optimization 5, / 15
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