Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Real-Time Bayesian Network Inference for Decision Support in Personnel Management: Report on Research Activities William H. Hsu, Computing and Information Sciences Haipeng Guo, Computing and Information Sciences Shing I Chang, Industrial and Manufacturing Systems Engineering Kansas State University http://groups.yahoo.com/group/onr-mpp This presentation is: http://www.kddresearch.org/KSU/CIS/ONR-2002-Jun-04.ppt Kansas State University Department of Computing and Information Sciences Overview • Knowledge Discovery in Databases (KDD) – Towards scalable data mining – Applications of KDD: learning and reasoning • Building Causal Models for Decision Support • Time Series and Model Integration – Prognostic (prediction and monitoring) applications – Crisis monitoring and simulation – Anomaly, intrusion, fraud detection – Web log analysis – Applying high-performance neural, genetic, Bayesian computation • Information Retrieval: Document Categorization, Text Mining – Business intelligence applications (e.g., patents) – “Web mining”: dynamic indexing and document analysis • High-Performance KDD Program at K-State Kansas State University Department of Computing and Information Sciences High-Performance Database Mining and KDD: Current Research Programs at K-State • Laboratory for Knowledge Discovery in Databases (KDD) – Research emphases: machine learning, reasoning under uncertainty – Applications • Decision support • Digital libraries and information retrieval • Remote sensing, robot vision and control • Human-Computer Interaction (HCI) - e.g., simulation-based training • Computational science and engineering (CSE) • Curriculum and Research Development – Real-time automated reasoning (inference) – Machine learning – Probabilistic models for multi-objective optimization – Intelligent displays: visualization of diagrammatic models – Knowledge-based expert systems, data modeling for KDD Kansas State University Department of Computing and Information Sciences Stages of Data Mining and Knowledge Discovery in Databases Kansas State University Department of Computing and Information Sciences Visual Programming: Java-Based Software Development Platform D2K © 2002 National Center for Supercomputing Applications (NCSA) Used with permission. Kansas State University Department of Computing and Information Sciences Bayesian Belief Networks (BBNS): Definition • Conditional Independence – X is conditionally independent (CI) from Y given Z (sometimes written X Y | Z) iff P(X | Y, Z) = P(X | Z) for all values of X, Y, and Z – Example: P(Thunder | Rain, Lightning) = P(Thunder | Lightning) T R | L • Bayesian Network – Directed graph model of conditional dependence assertions (or CI assumptions) – Vertices (nodes): denote events (each a random variable) – Edges (arcs, links): denote conditional dependencies • • n General Product (Chain) Rule for BBNs P X 1 , X 2 , , X n P X i | parents X i i 1 Example (“Sprinkler” BBN) Sprinkler: On, Off Season: Spring Summer X 1 Fall Winter X2 Ground: Wet, Dry X4 X3 X5 Ground: Slippery, Not-Slippery Rain: None, Drizzle, Steady, Downpour P(Summer, Off, Drizzle, Wet, Not-Slippery) = P(S) · P(O | S) · P(D | S) · P(W | O, D) · P(N | W) Kansas State University Department of Computing and Information Sciences Bayesian Networks and Recommender Systems • Current Research – Efficient BBN inference (parallel, multi-threaded Lauritzen-Spiegelhalter in D2K) – Hybrid quantitative and qualitative inference (“simulation”) – Continuous variables and hybrid (discrete/continuous) BBNs – Induction of hidden variables – Local structure: localized constraints and assumptions, e.g., Noisy-OR BBNs – Online learning • Incrementality (aka lifelong, situated, in vivo learning) • Ability to change network structure during inferential process – Polytree structure learning (tree decomposition): alternatives to Chow-Liu – Complexity of learning, inference in restricted classes of BBNs • Future Work – Decision networks aka influence diagrams (BBN + utility) – Anytime / real-time BBN inference for time-constrained decision support – Some temporal models: Dynamic Bayesian Networks (DBNs) Kansas State University Department of Computing and Information Sciences Data Mining: Development Cycle • • Model Identification – Queries: classification, assignment 60 – Specification of data model 50 – Grouping of attributes by type 40 Prediction Objective Identification Effort (%) • 30 – Assignment specification 20 – Identification of metrics 10 Reduction 0 – Refinement of data model Objective Determination Data Preparation Machine Learning Analysis & Assimilation – Selection of relevant data (quantitative, qualitative) • Synthesis: New Attributes • Integration: Multiple Data Sources (e.g., Enlisted Master File, Surveys) Environment (Data Model) Learning Element Knowledge Base Decision Support System Kansas State University Department of Computing and Information Sciences Learning Bayesian Networks: Gradient Ascent • Algorithm Train-BN (D) – Let wijk denote one entry in the CPT for variable Yi in the network • wijk = P(Yi = yij | parents(Yi) = <the list uik of values>) • e.g., if Yi Campfire, then (for example) uik <Storm = T, BusTourGroup = F> – WHILE termination condition not met DO // perform gradient ascent • Update all CPT entries wijk using training data D w ijk w ijk r Ph y ij , uik | x xD Storm w ijk Bus TourGroup • Renormalize wijk to assure invariants: w j ijk 1 Lightning Campfire j . 0 w ijk 1 • Applying Train-BN – Learns CPT values – Useful in case of known structure Thunder ForestFire – Key problems: learning structure from data, approximate inference Kansas State University Department of Computing and Information Sciences Scores for Learning Structure: The Role of Inference • General-Case BBN Structure Learning: Use Inference to Compute Scores • Recall: Bayesian Inference aka Bayesian Reasoning – Assumption: h H are mutually exclusive and exhaustive – Optimal strategy: combine predictions of hypotheses in proportion to likelihood • Compute conditional probability of hypothesis h given observed data D • i.e., compute expectation over unknown h for unseen cases • Let h structure, parameters CPTs P x m 1 | D P x 1 , x 2 , , x n | x 1 , x 2 , , x m P x m 1 | D, h P h | D hH Posterior Score Marginal Likelihood Prior over Parameters P h | D P D | h P h P h P D | h, Θ P Θ | h dΘ Prior over Structures Likelihood Kansas State University Department of Computing and Information Sciences Learning Structure: K2 Algorithm and ALARM • Algorithm Learn-BBN-Structure-K2 (D, Max-Parents) FOR i 1 to n DO // arbitrary ordering of variables {x1, x2, …, xn} WHILE (Parents[xi].Size < Max-Parents) DO // find best candidate parent Best argmaxj>i (P(D | xj Parents[xi]) // max Dirichlet score IF (Parents[xi] + Best).Score > Parents[xi].Score) THEN Parents[xi] += Best RETURN ({Parents[xi] | i {1, 2, …, n}}) • A Logical Alarm Reduction Mechanism [Beinlich et al, 1989] – BBN model for patient monitoring in surgical anesthesia – Vertices (37): findings (e.g., esophageal intubation), intermediates, observables – K2: found BBN different in only 1 edge from gold standard (elicited from expert) 10 19 6 5 4 21 20 27 15 11 29 28 25 1 2 26 3 7 13 23 8 12 16 36 31 32 17 18 22 34 35 3 7 24 9 33 14 30 Kansas State University Department of Computing and Information Sciences Major Software Releases, FY 2002 • Bayesian Network Tools in Java (BNJ) – v1.0a released Wed 08 May 2002 to www.Sourceforge.net – Key features • Standardized data format (XML) • Existing algorithms: inference, structure learning, data generation – Experimental results • Improved structure learning using K2, inference-based validation • Adaptive importance sampling (AIS) inference competitive with best published algorithms • Machine Learning in Java (MLJ) – v1.0a released Fri 10 May 2002 to www.Sourceforge.net – Key features: (3) inductive learning algorithms from MLC++, (2) inductive learning wrappers (1 from MLC++, 1 from GA literature) – Experimental results • Genetic wrappers for feature subset selection: Jenesis, MLJ-CHC • Overfitting control in supervised inductive learning for classification Kansas State University Department of Computing and Information Sciences Bayesian Network Tools in Java (BNJ) • About BNJ – v1.0a, 08 May 2002: 26000+ lines of Java code, GNU Public License (GPL) – http://www.kddresearch.org/Groups/Probabilistic-Reasoning/BNJ – Key features [Perry, Stilson, Guo, Hsu, 2002] • XML BN Interchange Format (XBN) converter – to serve 7 client formats (MSBN, Hugin, SPI, IDEAL, Ergo, TETRAD, Bayesware) • Full exact inference: Lauritzen-Spiegelhalter (Hugin) algorithm • Five (5) importance sampling algorithms: forward simulation (likelihood weighting) [Shachter and Peot, 1990], probabilistic logic sampling [Henrion, 1986], backward sampling [Fung and del Favero, 1995] selfimportance sampling [Shachter and Peot, 1990], adaptive importance sampling [Cheng and Druzdzel, 2000] • Data generator • Published Research with Applications to Personnel Science – Recent work • GA for improved structure learning: results in [HGPS02a; HGPS02b] • Real-time inference framework – multifractal analysis [GH02b] – Current work: prediction – migration trends (EMF); Sparse Candidate – Planned continuation: (dynamic) decision networks; continuous BNs Kansas State University Department of Computing and Information Sciences GA for BN Structure Learning [Hsu, Guo, Perry, Stilson, GECCO-2002] Dtrain (Inductive Learning) D: Training Data [B] Representation Evaluator for Learning Problems Dval (Inference) Ie : Inference Specification α f(α) Representation Candidate Fitness Representation [A] Genetic Algorithm Change of Representation and Inductive Bias Control α̂ Optimized Representation Kansas State University Department of Computing and Information Sciences Model-Based Validation [Hsu, Guo, Perry, Stilson, GECCO-2002] Dtrain (Model Training) [i] Inductive Learning (Parameter Estimation from Training Data) h Hypothesis Dval (Model Validation by Inference) Ie : Evidence Specification [ii] Validation (Measurement of Inferential Loss) [B] Representation Evaluator for Input Specifications f(α) α Specification Fitness (Inferential Loss) Candidate Input Specification Kansas State University Department of Computing and Information Sciences BNJ: Integrated Tool for Bayesian Network Learning and Inference XML Bayesian Network Learned from Data using K2 in BNJ Kansas State University Department of Computing and Information Sciences Machine Learning in Java (MLJ) • About MLJ – v1.0a, 10 May 2002: 24000+ lines of Java code, GNU Public License (GPL) – http://www.kddresearch.org/Groups/Machine-Learning/MLJ – Key features [Hsu, Schmidt, Louis, 2002] • Conformant to MLC++ input-output specification • Three (3) inductive learning algorithms: ID3, C4.5, discrete Naïve Bayes • Two (2) wrapper inducers: feature subset selection [Kohavi and John, 1997], CHC [Eshelman, 1990; Guerra-Salcedo and Whitley, 1999] • Published Research with Applications to Personnel Science – Recent work • Multi-agent learning [GH01, GH02a] • Genetic feature selection wrappers [HSL02, HWRC02, HS02] – Current work: WEKA compatibility, parallel online continuous arcing – Planned continuations • New inducers: instance-based (k-nearest-neighbor), sequential rule covering, feedforward artificial neural network (multi-layer perceptron) • New wrappers: theory-guided constructive induction, boosting (Arc-x4, AdaBoost.M1, POCA) • Integration of reinforcement learning (RL) inducers Kansas State University Department of Computing and Information Sciences Infrastructure for High-Performance Computation in Data Mining Rapid KDD Development Environment: Operational Overview Kansas State University Department of Computing and Information Sciences National Center for Supercomputing Applications (NCSA) D2K Kansas State University Department of Computing and Information Sciences Visual Programming Interface (Java): Parallel Genetic Algorithms Kansas State University Department of Computing and Information Sciences Time Series Modeling and Prediction: Integration with Information Visualization New Time Series Visualization System (Java3D) Kansas State University Department of Computing and Information Sciences Demographics-Based Clustering for Prediction (Continuing Research) DimensionalityReducing Projection (x’) Clusters of Similar Records Delaunay Triangulation Voronoi (Nearest Neighbor) Diagram (y) Cluster Formation and Segmentation Algorithm (Sketch) Kansas State University Department of Computing and Information Sciences Data Clustering in Interactive Real-Time Decision Support 15 × 15 Self-Organizing Map (U-Matrix Output) Cluster Map (Personnel Database) Kansas State University Department of Computing and Information Sciences Summary: State of High-Performance KDD at KSU-CIS • Laboratory for Knowledge Discovery in Databases (KDD) – Applications: interdisciplinary research programs at K-State, FY 2002 • Decision support, optimization (Hsu, CIS; Chang, IMSE) • (NSF EPSCoR) Bioinformatics – gene expression modeling (Hsu, CIS; Welch, Agronomy; Roe, Biology; Das, EECE) • Digital libraries, info retrieval (Hsu, CIS; Zollman, Physics; Math, Art) • Human-Computer Interaction (HCI) - e.g., simulation-based training • Curriculum Development – Real-time intelligent systems (Chang, Hsu, Neilsen, Singh) – Machine learning and artificial intelligence; info visualization (Hsu) – Other: bioinformatics, digital libraries, robotics, DBMS • Research Partnerships – NCSA: National Computational Science Alliance, National Center for Supercomputing Applications – Defense (ONR, ARL, DARPA), Industry (Raytheon) • Publications, More Info: http://www.kddresearch.org Kansas State University Department of Computing and Information Sciences