Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Multiple criteria facility location problems: A survey

Web-Based Education for All: A Tool for Development Adaptive Courseware.

In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from "orthogonal" trees with thresholds on individual features in every split, and one from "oblique" trees separating the feature space by randomly oriented hyperplanes. In spite of a rising interest in the random forest framework, however, ensembles built from orthogonal trees (RF) have gained most, if not all, attention so far.

In the present work we propose to employ "oblique" random forests (oRF) built from multivariate trees which explicitly learn optimal split directions at internal nodes using linear discriminative models, rather than using random coefficients as the original oRF. This oRF outperforms RF, as well as other classifiers, on nearly all data sets but those with discrete factorial features. Learned node models perform distinctively better than random splits. An oRF feature importance score shows to be preferable over standard RF feature importance scores such as Gini or permutation importance. The topology of the oRF decision space appears to be smoother and better adapted to the data, resulting in improved generalization performance. Overall, the oRF propose here may be preferred over standard RF on most learning tasks involving numerical and spectral data.

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On oblique random forests

Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

/pdf/parallel-surrogate-assisted-global-optimization-with-5bfsamfoyq.pdf

Parallel surrogate-assisted global optimization with expensive functions --- a survey

In this work we present the design of an automatic infant cry recognition system that classifies three different kinds of cries, which come from normal, deaf and asphyxiating infants, of ages from one day up to nine months old The classification is done through a pattern classifier, where the crying waves are taken as the input patterns We have experimented with patterns formed by vectors of Mel Frequency Cepstral Coefficients and Linear Prediction Coefficients The acoustic feature vectors are then processed, to be classified in their corresponding type of cry, through an Input Delay Neural Network, trained by gradient descent with adaptive learning rate back propagation algorithm To perform the experiments and to test the recognition system, we train the neural network with cries from randomly selected babies, and test it with a separate set of cries from babies selected only for testing Here, we present the design and implementation of the complete system, as well as the results from some experiments, which in the presented case are up to 86

/pdf/a-system-for-the-processing-of-infant-cry-to-recognize-45f06m9xjo.pdf

A System for the Processing of Infant Cry to Recognize Pathologies in Recently Born Babies with Neural Networks

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative). Samples were clinically labelled according to the results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle threshold and lymphocytes count from the patients. Our proposed generic method is a algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and deep artificial neural network classifier with convolutional layers called DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform proof-of-concept Web App CoughDetect to administer this test anonymously. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three severity levels. Our proposed web tool and underpinning algorithm for the robust, fast, point-of-need identification of Covid-19 facilitates the rapid detection of the infection. We believe that it has the potential to significantly hamper the Covid-19 pandemic across the world.

A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

Artificial Intelligence Arrives to the 21st Century.- Artificial Intelligence Arrives to the 21st Century.- Knowledge Representation and Reasoning.- Properties of Markovian Subgraphs of a Decomposable Graph.- Pre-conceptual Schema: A Conceptual-Graph-Like Knowledge Representation for Requirements Elicitation.- A Recognition-Inference Procedure for a Knowledge Representation Scheme Based on Fuzzy Petri Nets.- Inference Scheme for Order-Sorted Logic Using Noun Phrases with Variables as Sorts.- Answer Set General Theories and Preferences.- A Framework for the E-R Computational Creativity Model.- Fuzzy Logic and Fuzzy Control.- First-Order Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems.- Fuzzy State Estimation of Discrete Event Systems.- Real-Time Adaptive Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot.- Fuzzy-Based Adaptive Threshold Determining Method for the Interleaved Authentication in Sensor Networks.- A Fuzzy Logic Model for Software Development Effort Estimation at Personal Level.- Reconfigurable Networked Fuzzy Takagi Sugeno Control for Magnetic Levitation Case Study.- Automatic Estimation of the Fusion Method Parameters to Reduce Rule Base of Fuzzy Control Complex Systems.- A Fault Detection System Design for Uncertain T-S Fuzzy Systems.- Uncertainty and Qualitative Reasoning.- An Uncertainty Model for a Diagnostic Expert System Based on Fuzzy Algebras of Strict Monotonic Operations.- A Connectionist Fuzzy Case-Based Reasoning Model.- Error Bounds Between Marginal Probabilities and Beliefs of Loopy Belief Propagation Algorithm.- Applications of Gibbs Measure Theory to Loopy Belief Propagation Algorithm.- A Contingency Analysis of LeActiveMath's Learner Model.- Constructing Virtual Sensors Using Probabilistic Reasoning.- Solving Hybrid Markov Decision Processes.- Comparing Fuzzy Naive Bayes and Gaussian Naive Bayes for Decision Making in RoboCup 3D.- Using the Beliefs of Self-Efficacy to Improve the Effectiveness of ITS: An Empirical Study.- Qualitative Reasoning and Bifurcations in Dynamic Systems.- Evolutionary Algorithms and Swarm Intelligence.- Introducing Partitioning Training Set Strategy to Intrinsic Incremental Evolution.- Evolutionary Method for Nonlinear Systems of Equations.- A Multi-objective Particle Swarm Optimizer Hybridized with Scatter Search.- Neural Networks.- An Interval Approach for Weight's Initialization of Feedforward Neural Networks.- Aggregating Regressive Estimators: Gradient-Based Neural Network Ensemble.- The Adaptive Learning Rates of Extended Kalman Filter Based Training Algorithm for Wavelet Neural Networks.- Multistage Neural Network Metalearning with Application to Foreign Exchange Rates Forecasting.- Genetic Optimizations for Radial Basis Function and General Regression Neural Networks.- Complexity of Alpha-Beta Bidirectional Associative Memories.- A New Bi-directional Associative Memory.- Optimization and Scheduling.- A Hybrid Ant Algorithm for the Airline Crew Pairing Problem.- A Refined Evaluation Function for the MinLA Problem.- ILS-Perturbation Based on Local Optima Structure for the QAP Problem.- Application of Fuzzy Multi-objective Programming Approach to Supply Chain Distribution Network Design Problem.- Route Selection and Rate Allocation Using Evolutionary Computation Algorithms in Multirate Multicast Networks.- A Polynomial Algorithm for 2-Cyclic Robotic Scheduling.- A New Algorithm That Obtains an Approximation of the Critical Path in the Job Shop Scheduling Problem.- A Quay Crane Scheduling Method Considering Interference of Yard Cranes in Container Terminals.- Comparing Schedule Generation Schemes in Memetic Algorithms for the Job Shop Scheduling Problem with Sequence Dependent Setup Times.- A Fuzzy Set Approach for Evaluating the Achievability of an Output Time Forecast in a Wafer Fabrication Plant.- Machine Learning and Feature Selection.- How Good Are the Bayesian Information Criterion and the Minimum Description Length Principle for Model Selection? A Bayesian Network Analysis.- Prediction of Silkworm Cocoon Yield in China Based on Grey-Markov Forecasting Model.- A Novel Hybrid System with Neural Networks and Hidden Markov Models in Fault Diagnosis.- Power System Database Feature Selection Using a Relaxed Perceptron Paradigm.- Feature Elimination Approach Based on Random Forest for Cancer Diagnosis.- On Combining Fractal Dimension with GA for Feature Subset Selecting.- Locally Adaptive Nonlinear Dimensionality Reduction.- Classification.- Fuzzy Pairwise Multiclass Support Vector Machines.- Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets.- Optimizing Weighted Kernel Function for Support Vector Machine by Genetic Algorithm.- Decision Forests with Oblique Decision Trees.- Using Reliable Short Rules to Avoid Unnecessary Tests in Decision Trees.- Selection of the Optimal Wavebands for the Variety Discrimination of Chinese Cabbage Seed.- Hybrid Method for Detecting Masqueraders Using Session Folding and Hidden Markov Models.- Toward Lightweight Detection and Visualization for Denial of Service Attacks.- Tri-training and Data Editing Based Semi-supervised Clustering Algorithm.- Knowledge Discovery.- Automatic Construction of Bayesian Network Structures by Means of a Concurrent Search Mechanism.- Collaborative Design Optimization Based on Knowledge Discovery from Simulation.- Behavioural Proximity Approach for Alarm Correlation in Telecommunication Networks.- The MineSP Operator for Mining Sequential Patterns in Inductive Databases.- Visual Exploratory Data Analysis of Traffic Volume.- Computer Vision.- A Fast Model-Based Vision System for a Robot Soccer Team.- Statistics of Visual and Partial Depth Data for Mobile Robot Environment Modeling.- Automatic Facial Expression Recognition with AAM-Based Feature Extraction and SVM Classifier.- Principal Component Net Analysis for Face Recognition.- Advanced Soft Remote Control System Using Hand Gesture.- IMM Method Using Tracking Filter with Fuzzy Gain.- Image Processing and Image Retrieval.- Complete FPGA Implemented Evolvable Image Filters.- Probabilistic Rules for Automatic Texture Segmentation.- A Hybrid Segmentation Method Applied to Color Images and 3D Information.- Segmentation of Medical Images by Using Wavelet Transform and Incremental Self-Organizing Map.- Optimal Sampling for Feature Extraction in Iris Recognition Systems.- Histograms, Wavelets and Neural Networks Applied to Image Retrieval.- Adaptive-Tangent Space Representation for Image Retrieval Based on Kansei.- Natural Language Processing.- Distributions of Functional and Content Words Differ Radically.- Speeding Up Target-Language Driven Part-of-Speech Tagger Training for Machine Translation.- Defining Classifier Regions for WSD Ensembles Using Word Space Features.- Impact of Feature Selection for Corpus-Based WSD in Turkish.- Spanish All-Words Semantic Class Disambiguation Using Cast3LB Corpus.- An Approach for Textual Entailment Recognition Based on Stacking and Voting.- Textual Entailment Beyond Semantic Similarity Information.- On the Identification of Temporal Clauses.- Issues in Translating from Natural Language to SQL in a Domain-Independent Natural Language Interface to Databases.- Information Retrieval and Text Classification.- Interlinguas: A Classical Approach for the Semantic Web. A Practical Case.- A Fuzzy Embedded GA for Information Retrieving from Related Data Set.- On Musical Performances Identification, Entropy and String Matching.- Adaptive Topical Web Crawling for Domain-Specific Resource Discovery Guided by Link-Context.- Evaluating Subjective Compositions by the Cooperation Between Human and Adaptive Agents.- Using Syntactic Distributional Patterns for Data-Driven Answer Extraction from the Web.- Applying NLP Techniques and Biomedical Resources to Medical Questions in QA Performance.- Fast Text Categorization Based on a Novel Class Space Model.- A High Performance Prototype System for Chinese Text Categorization.- A Bayesian Approach to Classify Conference Papers.- An Ontology Based for Drilling Report Classification.- Topic Selection of Web Documents Using Specific Domain Ontology.- Speech Processing.- Speech Recognition Using Energy, MFCCs and Rho Parameters to Classify Syllables in the Spanish Language.- Robust Text-Independent Speaker Identification Using Hybrid PCA&LDA.- Hybrid Algorithm Applied to Feature Selection for Speaker Authentication.- Using PCA to Improve the Generation of Speech Keys.- Multiagent Systems.- Verifying Real-Time Temporal, Cooperation and Epistemic Properties for Uncertain Agents.- Regulating Social Exchanges Between Personality-Based Non-transparent Agents.- Using MAS Technologies for Intelligent Organizations: A Report of Bottom-Up Results.- Modeling and Simulation of Mobile Agents Systems Using a Multi-level Net Formalism.- Using AI Techniques for Fault Localization in Component-Oriented Software Systems.- Robotics.- Exploring Unknown Environments with Randomized Strategies.- Integration of Evolution with a Robot Action Selection Model.- A Hardware Architecture Designed to Implement the GFM Paradigm.- Bioinformatics and Medical Applications.- Fast Protein Structure Alignment Algorithm Based on Local Geometric Similarity.- Robust EMG Pattern Recognition to Muscular Fatigue Effect for Human-Machine Interaction.- Classification of Individual and Clustered Microcalcifications in Digital Mammograms Using Evolutionary Neural Networks.- Heart Cavity Detection in Ultrasound Images with SOM.- An Effective Method of Gait Stability Analysis Using Inertial Sensors.

MICAI 2006: Advances in Artificial Intelligence

MICAI 2006 : advances in artificial intelligence : 5th Mexican International Conference on Artificial Intelligence Apizaco, Mexico, November 13-17, 2006 : proceedings

Evolutionary algorithms have gained popularity as an alternative for dealing with multi-objective optimization problems. However, these algorithms require to perform a relatively high number of fitness function evaluations in order to generate a reasonably good approximation of the Pareto front. This can be a shortcoming when fitness evaluations are computationally expensive. In this paper, we propose an approach that combines an evolutionary algorithm with an ensemble of surrogate models based on support vector machines (SVM), which are used to approximate the fitness functions of a problem. The proposed approach performs a model selection process for determining the appropriate hyperparameters values for each SVM in the ensemble. The ensemble is constructed in an incremental fashion, such that the models are updated with the knowledge gained during the evolutionary process, but the information from previous evaluated regions is also preserved. A criterion based on surrogate fidelity is also proposed for determining when should the surrogates be updated. We evaluate the performance of our proposal using a benchmark of test problems widely used in the literature and we compare our results with respect to those obtained by the NSGA-II. Our proposed approach is able to significantly reduce the number of fitness function evaluations performed, while producing solutions which are close to the true Pareto front.

Carlos A. Reyes-García

Papers

A System for the Processing of Infant Cry to Recognize Pathologies in Recently Born Babies with Neural Networks

A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

MICAI 2006: Advances in Artificial Intelligence

MICAI 2006 : advances in artificial intelligence : 5th Mexican International Conference on Artificial Intelligence Apizaco, Mexico, November 13-17, 2006 : proceedings

A hybrid surrogate-based approach for evolutionary multi-objective optimization