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A Novel Mixed Values k-Prototypes Algorithm with Application to Health Care databases Mining 2

A Novel Mixed Values <i>k</i>-Prototypes Algorithm with Appl...

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ieee symposium on computational intelligence in Healthcare and e-health

作者： Najjar, Ahmed Gagne, Christian Reinharz, Daniel Univ Laval LVSN Dept Genie Elect & Genie Informat Quebec City PQ Canada Univ Laval LSD Dept Med Sociale & Prevent Quebec City PQ Canada

ISBN: (纸本)9781479953752

The current availability of large datasets composed of heterogeneous objects stresses the importance of large-scale clustering of mixed complex items. Several algorithms have been developed for mixed datasets composed of numerical and categorical variables, a well-known algorithm being the k-prototypes. This algorithm is efficient for clustering large datasets given its linear complexity. However, many fields are handling more complex data, for example variable-size sets of categorical values mixed with numerical and categorical values, which cannot be processed as is by the k-prototypes algorithm. We are proposing a variation of the k-prototypes clustering algorithm that can handle these complex entities, by using a bag-of-words representation for the multi valued categorical variables. We evaluate our approach on a real-world application to the clustering of administrative health care databases in Quebec, with results illustrating the good performances of our method.

关键词： computational complexity data mining database management systems medical information systems pattern clustering Quebec bag-of-words representation health care databases mining heterogeneous objects k-prototypes clustering algorithm large-scale clustering linear complexity mixed complex items mixed values k-prototypes algorithm valued categorical variables variable-size categorical values sets Clustering algorithms Complexity theory Equations Medical services medical information systems linear complexity Clustering algorithms physician services database management systems Cluster Analysis Complexity theory Quebec complexity classes data mining Health Care Medicine

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Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems 5

Precision-Recall-Optimization in Learning Vector Quantizatio...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Villmann, Thomas Kaden, Marika Lange, Mandy Stuermer, Paul Hermann, Wieland Univ Appl Sci Mittweida Comp Intelligence Grp Technikumpl 17 D-09648 Mittweida Germany Paracelsus Hosp Zwickau Dept Neurol D-08060 Zwickau Germany

ISBN: (纸本)9781479945184

Classification and decision systems in data analysis are mostly based on accuracy optimization. This criterion is only a conditional informative value if the data are imbalanced or false positive/negative decisions cause different costs. Therefore more sophisticated statistical quality measures are favored in medicine, like precision, recall etc.. Otherwise, most classification approaches in machine learning are designed for accuracy optimization. In this paper we consider variants of learning vector quantizers (LVQs) explicitly optimizing those advanced statistical quality measures while keeping the basic intuitive ingredients of these classifiers, which are the prototype based principle and the Hebbian learning. In particular we focus in this contribution particularly to precision and recall as important measures for use in medical applications. We investigate these problems in terms of precision-recall curves as well as receiver-operating characteristic (ROC) curves well-known in statistical classification and test analysis. With the underlying more general framework, we provide a principled alternatives traditional classifiers, such that a closer connection to statistical classification analysis can be drawn.

关键词： Hebbian learning data analysis medical computing optimisation pattern classification statistical testing vector quantisation decision systems learning vector quantization classifiers machine learning medical applications medical classification systems precision-recall curves precision-recall-optimization receiver-operating characteristic curves statistical classification analysis statistical quality measures test analysis Accuracy Artificial neural networks Medical diagnostic imaging Optimization Prototypes Standards Vectors Hebbian principle decision system Test Theory Vector quantization medical computing Statistical tests data analysis Pattern recognition ROC Curve PROTOTYPE Artificial neural networks Classifiers diagnostic imaging

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Batch Linear Least Squares-based Learning Algorithm for MLMVN with Soft Margins 5

Batch Linear Least Squares-based Learning Algorithm for MLMV...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Aizenberg, Evgeni Aizenberg, Igor Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Netherlands Texas A&M Univ Texarkana Comp Sci Texarkana TX USA Texas A&M Univ Texarkana Computat Intelligence Lab Texarkana TX USA

ISBN: (纸本)9781479945184

In this paper, we consider a batch learning algorithm for the multilayer neural network with multi-valued neurons (MLMVN) and its soft margins variant (MLMVN-SM). MLMVN is a neural network with a standard feedforward organization based on the multi-valued neuron (MVN). MVN is a neuron with complex-valued weights and inputs/output located on the unit circle. Standard MLMVN has a derivative-free learning algorithm based on the error-correction learning rule. Recently, this algorithm was modified for MLMVN with discrete outputs by using soft margins (MLMVN-SM). This modification improves classification results when MLMVN is used as a classifier. Another recent development in MLMVN is the use of batch acceleration step for MLMVN with a single output neuron. Complex QR-decomposition was used to adjust the output neuron weights for all learning samples simultaneously, while the hidden neuron weights were adjusted in a regular way. In this paper, we merge the soft margins approach with batch learning. We suggest a batch linear least squares (LLS) learning algorithm for MLMVN-SM. We also expand the batch technique to multiple output neurons and hidden neurons. This new learning technique drastically reduces the number of learning iterations and learning time when solving classification problems (compared to MLMVN-SM), while maintaining the classification accuracy of MLMVN-SM.

关键词： complex-valued neural networks multi-valued neuron MLMVN batch learning soft margins LLS

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Joint decision-making on two visual perception systems

Joint decision-making on two visual perception systems

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ieee symposium on computational intelligence, Cognitive Algorithms, Mind, and Brain

作者： Valim, Henrique Clemens, Molly Hsu, D. Frank Fordham Univ Dept Comp & Informat Sci Lab Informat & Data Min New York NY 10023 USA

ISBN: (纸本)9781479945498

Decision-making is an interdisciplinary problem that has been the focus of many studies, particularly by interactive pairs of visual cognition systems. In a series of experiments, Bahrami et al. (2010) showed that dyadic interaction is beneficial only if participants communicate with each other about their confidence in making a judgment. Aside from data combination using both simple and weighted average, Hsu et al. (2006) first described the use of combinatorial fusion to combine multiple scoring systems (MSS). In this experiment, sixteen trials were conducted using pairs of individuals as visual cognition systems. Participants observed a target being thrown in a grassy field which could not be seen once it had landed, allowing them to then independently perceive the position of the target and determine their confidence level. The results of these trials were analyzed for performance of score and rank combinations relative to both the original cognition systems of the individuals and to simple and weighted averages of their systems. We demonstrated, using combinatorial fusion, that the combination of two visual perception systems is better than each of the individual systems only if they perform relatively well and they are diverse.

关键词： cognition decision making visual perception MSS combinatorial fusion confidence level data combination dyadic interaction grassy field individual systems interactive pairs interdisciplinary problem joint decision-making multiple scoring systems original cognition systems rank combination performance score performance system simple averages system weighted averages target position visual cognition systems visual perception systems Cognition Decision making Equations Joints Mathematical model Visual perception Visualization Visual Perception dyadic interaction Cognition Sight intelligence decision making Joints Joints Visualization Manuscripts Spiroplasma kunkelii Mathematical Model

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Learning Fuzzy Control Parameters for Kite-based Tethered Flying Robot using Human Operation data 7

Learning Fuzzy Control Parameters for Kite-based Tethered Fl...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International symposium on Advanced Intelligent Systems (ISIS)

作者： Todoroki, Chiaki Takahashi, Yasutake Nakamura, Takayuki Univ Fukui Grad Sch Engn Dept Human & Artificial Intelligent Syst 3-9-1 Bunkyo Fukui Fukui 9108507 Japan Wakayama Univ Fac Syst Engn Dept Comp & Commun Sci Sakaetani 930 Wakayama 6408510 Japan

ISBN: (纸本)9781479959556

This research aims at realizing a flying observation system which complements other information gathering systems using a balloon or an air vehicle. We have proposed the kite-based tethered flying robot with long-term activity capability[1]. This paper shows a computational model of the kite-based tethered flying robot and a method of learning fuzzy control parameters for the robot using human operation data.

关键词： aerospace robotics aircraft balloons control engineering computing fuzzy control learning (artificial intelligence) air vehicle balloon flying observation system human operation data information gathering systems kite-based tethered flying robot learning fuzzy control parameters computational modeling Drag Force Mathematical model Robots Wind speed Windings

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To What Extend Can We Predict Students' Performance? A Case Study in Colleges in South Africa 5

To What Extend Can We Predict Students' Performance? A Case ...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Poh, Norman Smythe, Ian Univ Surrey Dept Comp Guildford Surrey England Do IT Solut Ltd Cardiff S Glam Wales

ISBN: (纸本)9781479945184

Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results;(2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether;(3) from other test results + covariates (combination of (1) and (2));and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.

关键词： computer aided instruction educational institutions principal component analysis socio-economic effects English comprehension test result PCA South African college student academic test result colleges dimensionality reduction governments learning difficulty social policy socio-economic background standard error student performance computational modeling Context Educational institutions Field effect transistors Predictive models Standards Educational institutions Learning difficulties colleges principal components analysis Standard error Social Policy dimensionality reduction computer-assisted instruction socio-economic effects Field effect transistors Government computational modeling Prediction models Passive Cutaneous Anaphylaxis Students

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Proceedings of 2014 ieee 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI∗CC 2014

Proceedings of 2014 IEEE 13th International Conference on Co...

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13th ieee International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014

ISBN: (纸本)9781479960811

The proceedings contain 80 papers. The topics discussed include: from information revolution to intelligence revolution: big data science vs. intelligence science;polyscale analysis and fractional operators for cognitive systems;towards a differential diagnostic of PTSD using cognitive computing methods;big data and societal challenges;overcoming the fear of semantics;non-Kolmogorov probability and its use for constructing a model of human perception process;position update mechanisms for enhanced particle swarm classification;cognitive reasoning and inferences through psychologically based personalised modelling of emotions using associative classifiers;functional networks of anatomic brain regions;cognitive identification of systems using nonlinear dynamics;a bio-inspired architecture of the vestibular system for body balance of a humanoid robot;rank level fusion of multimodal cancelable biometrics;and a cognitive approach for texture analysis using neighbors-based binary patterns.

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Large-Scale Methodological Comparison of Acute Hypotensive Episode Forecasting Using MIMIC2 Physiological Waveforms

Large-Scale Methodological Comparison of Acute Hypotensive E...

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27th ieee International symposium on Computer-Based Medical Systems (CBMS)

作者： Kim, Yongwook Bryce Seo, Joohyun O'Reilly, Una-May MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA MIT Microsyst Technol Labs Cambridge MA 02139 USA

ISBN: (纸本)9781479944354

We compare the dynamic Bayesian network and k-nearest neighbor-based predictors for the occurrence of acute hypotensive episodes (AHE) with respect to various data conditions (size, class balance ratio) and problem definition settings (lag, lead time). From our dataset extracted from the large ICU physiological waveform repository of MIMIC2 database, we find that both models are effective for predicting AHE and their performances improve with increasing training dataset size. We also empirically demonstrate that the nearest neighbor method has a better performance for larger datasets in terms of both prediction result and computational time, but it severely degrades for class imbalanced data while the Bayesian network remains robust.

关键词： data models Hidden Markov models Physiology Sensitivity Time series analysis Training Training data data models Training data Hidden Markov models Bayesian networks time series analysis Physiology Functional training nearest neighbor algorithm hypotensive episode dataset waveforms Training LEAD TIME

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Bisection-based Merging Algorithm for Creation of One-Sided Concept Lattices

Bisection-based Merging Algorithm for Creation of One-Sided ...

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ieee 12th International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Butka, P. Pocsova, J. Pocs, J. Tech Univ Kosice Fac Elect Engn & Informat Dept Cybernet & Artificial Intelligence Kosice Slovakia Tech Univ Kosice BERG Fac Inst Control & Informatizat Prod Proc Kosice Slovakia Palacky Univ Fac Sci Dept Algebra & Geometry CR-77147 Olomouc Czech Republic Slovak Acad Sci Kosice Slovakia

ISBN: (纸本)9781479934423

In this paper we provide the new version of algorithm for creation model called Generalized One-Sided Concept Lattice (GOSCL). This model provides the specific fuzzy version of data analytical method based on the approach known as Formal Concept Analysis (FCA), which supports data tables containing the multiple types of attributes defined as fuzzy sets. The acquisition of the FCA models is computationally complex task and it is important to find more effective algorithms for their creation. Therefore, we have designed the algorithm for the reduction of the computation times, which is based on the simple division of input data table using bisection-based approach and then merging procedure compose the local models into one finally merged concept lattice for the complete input data. We present the illustrative experiments which prove the applicability of the presented algorithm for sparse data inputs, where it is possible to get significant decrease of computation times. More effective algorithm for sparse data can be useful for the application of FCA-based models in sparse domains like information retrieval or text analysis.

关键词： Concept Lattice Sparse data computational time merger creation efficient algorithm text processing algorithms information retrieval

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Convex Multi-task Relationship Learning using Hinge Loss 5

Convex Multi-task Relationship Learning using Hinge Loss

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Charuvaka, Anveshi Rangwala, Huzefa George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781479945184

Multi-task learning improves generalization performance by learning several related tasks jointly. Several methods have been proposed for multi-task learning in recent years. Many methods make strong assumptions about symmetric task relationships while some are able to utilize externally provided task relationships. However, in many real world tasks the degree of relatedness among tasks is not known a priori. Methods which are able to extract the task relationships and exploit them while simultaneously learning models with good generalization performance can address this limitation. In the current work, we have extended a recently proposed method for learning task relationships using smooth squared loss for regression to classification problems using non-smooth hinge loss due to the demonstrated effectiveness of SVM classifier in single task classification settings. We have also developed an efficient optimization procedure using bundle methods for the proposed multi-task learning formulation. We have validated our method on one simulated and two real world datasets and have compared its performance to competitive baseline single-task and multi-task methods.

关键词： convex programming generalisation (artificial intelligence) learning (artificial intelligence) pattern classification support vector machines SVM classifier bundle methods classification problems convex multitask relationship learning generalization performance generalization performance improvement learning task relationships multitask methods nonsmooth hinge loss optimization procedure real world datasets real world tasks relatedness degree simultaneous learning models single task classification methods single-task methods smooth squared loss symmetric task relationship extraction Approximation methods Fasteners Linear programming Optimization Risk management Support vector machines Training Support Vector Network Optimization Procedure Fasteners convex programming Approximation method Pattern recognition Risk Management linear programming Functional training Hinge

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