A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application

By ''fusion'' this work means integration of disparate types of data including (intervals of) real numbers as well as possibility/probability distributions defined over the totally-ordered lattice (R,= [0,1] result in the Cartesian product lattice (F^N,@?) towards decision-making based on reasoning. In conclusion, the space (F^N,@?) emerges as a formal framework for the development of hybrid intelligent fusion systems/schemes. A fuzzy lattice reasoning (FLR) ensemble scheme, namely FLR pairwise ensemble, or FLRpe for short, is introduced here for sound decision-making based on descriptive knowledge (rules). Advantages include the sensible employment of a sparse rule base, employment of granular input data (to cope with imprecision/uncertainty/vagueness), and employment of all-order data statistics. The advantages as well as the performance of our proposed techniques are demonstrated, comparatively, by computer simulation experiments regarding an industrial dispensing application.

[1]  Ludmila I. Kuncheva,et al.  Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Gail A. Carpenter,et al.  Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks , 2005, Neural Networks.

[3]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[5]  Xiongbiao Chen,et al.  Modeling and control of fluid dispensing processes: a state-of-the-art review , 2009 .

[6]  Bernardete Ribeiro,et al.  Distributed Text Classification With an Ensemble Kernel-Based Learning Approach , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Yuchou Chang,et al.  CBIR of spine X-ray images on inter-vertebral disc space and shape profiles using feature ranking and voting consensus , 2009, Data Knowl. Eng..

[8]  Daniel Hernández-Lobato,et al.  An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Ping Zhong,et al.  A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Vassilis G. Kaburlasos.  FINs: lattice theoretic tools for improving prediction of sugar production from populations of measurements , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Bruce A. Pearce,et al.  A model-free ensemble method for class prediction with application to biomedical decision making , 2009, Artif. Intell. Medicine.

[12]  D. Angelaki,et al.  Multisensory integration: resolving sensory ambiguities to build novel representations , 2010, Current Opinion in Neurobiology.

[13]  Mohamed S. Kamel,et al.  On voting-based consensus of cluster ensembles , 2010, Pattern Recognit..

[14]  Vassilis G. Kaburlasos,et al.  Granular self-organizing map (grSOM) for structure identification , 2006, Neural Networks.

[15]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[16]  Michalis E. Zervakis,et al.  Combination of multiple classifiers for post-placement quality inspection of components: A comparative study , 2010, Inf. Fusion.

[17]  Xiaodong Lin,et al.  Active Learning From Stream Data Using Optimal Weight Classifier Ensemble , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Sohyung Cho,et al.  achine ensemble approach for simultaneous detection of transient and radual abnormalities in end milling using multisensor fusion , 2009 .

[19]  C. Alcalde,et al.  A constructive method for the definition of interval-valued fuzzy implication operators , 2005, Fuzzy Sets Syst..

[20]  Samy Bengio,et al.  Automatic analysis of multimodal group actions in meetings , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Giorgio Valentini,et al.  Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines , 2010, Neurocomputing.

[22]  Athanasios Kehagias,et al.  A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms , 2003, Journal of Intelligent Information Systems.

[23]  Manuel Graña,et al.  A lattice computing approach for on-line fMRI analysis , 2010, Image Vis. Comput..

[24]  Plamen P. Angelov,et al.  Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section , 2008, IEEE Transactions on Fuzzy Systems.

[25]  Michal Wozniak,et al.  Proposition of common classifier construction for pattern recognition with context task , 2006, Knowl. Based Syst..

[26]  Hui Li,et al.  Majority voting combination of multiple case-based reasoning for financial distress prediction , 2009, Expert Syst. Appl..

[27]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[28]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[29]  Michael Georgiopoulos,et al.  Order of Search in Fuzzy ART and Fuzzy ARTMAP: Effect of the Choice Parameter , 1996, Neural Networks.

[30]  Vladik Kreinovich,et al.  Handbook of Granular Computing , 2008 .

[31]  Kaoru Hirota,et al.  Inference Based on alpha-Cut and Generalized Mean with Fuzzy Tautological Rules , 2010, J. Adv. Comput. Intell. Intell. Informatics.

[32]  Christian Ronse,et al.  Why mathematical morphology needs complete lattices , 1990, Signal Process..

[33]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[34]  Pericles A. Mitkas,et al.  Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation , 2007, Int. J. Approx. Reason..

[35]  Lotfi A. Zadeh,et al.  From Computing with Numbers to Computing with Words - from Manipulation of Measurements to Manipulation of Perceptions , 2005, Logic, Thought and Action.

[36]  Vassilios Petridis,et al.  FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples , 2003, J. Mach. Learn. Res..

[37]  Manuel Graña.  Lattice computing and natural computing , 2009, Neurocomputing.

[38]  Ludmila I Kuncheva,et al.  Classifier ensembles for fMRI data analysis: an experiment. , 2010, Magnetic resonance imaging.

[39]  Vassilis G. Kaburlasos.  Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications (Studies in Computational Intelligence) , 2006 .

[40]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[41]  Marco Wiering,et al.  Ensemble Algorithms in Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[42]  Cliff Joslyn,et al.  Order Metrics for Semantic Knowledge Systems , 2010, HAIS.

[43]  Vassilios Petridis,et al.  Fuzzy Lattice Neurocomputing (FLN) models , 2000, Neural Networks.

[44]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[45]  Matthew C. Casey,et al.  Special Issue on Biologically-Inspired Information Fusion , 2010, Inf. Fusion.

[46]  Vassilis G. Kaburlasos,et al.  Granular Enhancement of Fuzzy ART/SOM Neural Classifiers Based on Lattice Theory , 2007, Computational Intelligence Based on Lattice Theory.

[47]  Amine Bermak,et al.  Gas Identification Based on Committee Machine for Microelectronic Gas Sensor , 2006, IEEE Transactions on Instrumentation and Measurement.

[48]  James Llinas,et al.  A case study with design of experiments: Performance evaluation methodology for Level 1 distributed data fusion processes , 2011, Inf. Fusion.

[49]  Sylvie Galichet,et al.  A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals , 2010, Inf. Sci..

[50]  Éloi Bossé,et al.  Robust combination rules for evidence theory , 2009, Inf. Fusion.

[51]  Thierry Denoeux,et al.  Ensemble clustering in the belief functions framework , 2011, Int. J. Approx. Reason..

[52]  Vassilis G. Kaburlasos,et al.  Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals' numbers (INs) , 2010, Inf. Sci..

[53]  Gail A. Carpenter,et al.  Self-supervised ARTMAP , 2010, Neural Networks.

[54]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[55]  Peter Sussner,et al.  Morphological perceptrons with competitive learning: Lattice-theoretical framework and constructive learning algorithm , 2011, Inf. Sci..

[56]  Manuel Graña,et al.  Lattice independent component analysis for functional magnetic resonance imaging , 2011, Inf. Sci..

[57]  Athanasios Kehagias,et al.  Novel fuzzy inference system (FIS) analysis and design based on lattice theory. Part I: Working principles , 2006, Int. J. Gen. Syst..

[58]  Eyke Hüllermeier,et al.  Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting , 2010, Pattern Recognit..

[59]  Athanasios Kehagias,et al.  Novel Fuzzy Inference System (FIS) Analysis and Design Based on Lattice Theory , 2007, IEEE Transactions on Fuzzy Systems.

[60]  Robert Sabourin,et al.  Overfitting cautious selection of classifier ensembles with genetic algorithms , 2009, Inf. Fusion.

[61]  Kaoru Hirota,et al.  Suppression Effect of alpha-Cut Based Inference on Consequence Deviations , 2010, J. Adv. Comput. Intell. Intell. Informatics.

[62]  Vassilis G. Kaburlasos,et al.  A granular extension of the fuzzy-ARTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR) , 2009, Neurocomputing.

[63]  Kagan Tumer,et al.  Applications of ensemble methods , 2008, Information Fusion.

[64]  Juan M. Corchado,et al.  Hybrid learning machines , 2009, Neurocomputing.

[65]  Naim Dahnoun,et al.  Studies in Computational Intelligence , 2013 .

[66]  Mauro Ursino,et al.  A theoretical study of multisensory integration in the superior colliculus by a neural network model , 2008, Neural Networks.

[67]  Vassilis G. Kaburlasos,et al.  Granular Fuzzy Inference System (FIS) Design by Lattice Computing , 2010, HAIS.

[68]  Gail A. Carpenter,et al.  ARTMAP neural networks for information fusion and data mining: map production and target recognition methodologies , 2003, Neural Networks.

[69]  Aníbal R. Figueiras-Vidal,et al.  Committees of Adaboost ensembles with modified emphasis functions , 2010, Neurocomputing.

[70]  George A. Lampropoulos,et al.  An optimized architecture for classification combining data fusion and data-mining , 2007, Inf. Fusion.

[71]  Manuel Graña,et al.  Two lattice computing approaches for the unsupervised segmentation of hyperspectral images , 2009, Neurocomputing.

[72]  Kiyohiko Uehara,et al.  Fuzzy inference based on families of α-level sets , 1993, IEEE Trans. Fuzzy Syst..

[73]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[74]  Ludmila I. Kuncheva.  Diversity in multiple classifier systems , 2005, Inf. Fusion.

[75]  Adrião Duarte Dória Neto,et al.  Classification of multispectral images in coral environments using a hybrid of classifier ensembles , 2010, Neurocomputing.

[76]  Albert Ali Salah,et al.  Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms , 2009, IEEE Transactions on Information Forensics and Security.

[77]  Kaoru Hirota,et al.  Parallel and Multistage Fuzzy Inference Based on Families of alpha-level sets , 1998, Inf. Sci..

[78]  Etienne E. Kerre,et al.  On the role of complete lattices in mathematical morphology: From tool to uncertainty model , 2011, Inf. Sci..

[79]  Michael I. Jordan,et al.  Convergence results for the EM approach to mixtures of experts architectures , 1995, Neural Networks.

[80]  Xue Wang,et al.  Compacted Probabilistic Visual Target Classification With Committee Decision in Wireless Multimedia Sensor Networks , 2009 .

[81]  Emilio Corchado,et al.  Hybrid intelligent algorithms and applications , 2010, Inf. Sci..

[82]  Josef Kittler,et al.  Sum Versus Vote Fusion in Multiple Classifier Systems , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[83]  Zhi-Zhong Mao,et al.  An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace , 2010, IEEE Transactions on Automation Science and Engineering.

[84]  C. K. Kwong,et al.  The process modelling of epoxy dispensing for microchip encapsulation using fuzzy linear regression with fuzzy intervals , 2003 .