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Mach. Learn. Knowl. Extr., Volume 1, Issue 1 (December 2019)

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Open AccessArticle Using the Outlier Detection Task to Evaluate Distributional Semantic Models
Mach. Learn. Knowl. Extr. 2018, 1(1), 211-223; https://doi.org/10.3390/make1010013
Received: 2 August 2018 / Revised: 16 November 2018 / Accepted: 19 November 2018 / Published: 22 November 2018
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Abstract
In this article, we define the outlier detection task and use it to compare neural-based word embeddings with transparent count-based distributional representations. Using the English Wikipedia as a text source to train the models, we observed that embeddings outperform count-based representations when their
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In this article, we define the outlier detection task and use it to compare neural-based word embeddings with transparent count-based distributional representations. Using the English Wikipedia as a text source to train the models, we observed that embeddings outperform count-based representations when their contexts are made up of bag-of-words. However, there are no sharp differences between the two models if the word contexts are defined as syntactic dependencies. In general, syntax-based models tend to perform better than those based on bag-of-words for this specific task. Similar experiments were carried out for Portuguese with similar results. The test datasets we have created for the outlier detection task in English and Portuguese are freely available. Full article
(This article belongs to the Special Issue Language Processing and Knowledge Extraction)
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Open AccessOpinion Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods
Mach. Learn. Knowl. Extr. 2018, 1(1), 205-210; https://doi.org/10.3390/make1010012
Received: 26 September 2018 / Revised: 18 November 2018 / Accepted: 21 November 2018 / Published: 22 November 2018
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Abstract
We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions
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We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions of measurements on the genomics level. However, to translate these data into meaningful information, the data need to be analyzable. The first step for such an analysis is the deliberate selection of subsets of raw data for studying dedicated research questions. Unfortunately, this is a non-trivial problem when millions of individual data files are available with an intricate connection structure induced by experimental dependencies. In this paper, we argue for the need to introduce such search capabilities for big genomics data repositories with a specific discussion about LINCS. Specifically, we suggest the introduction of smart interfaces allowing the exploitation of the connections among individual raw data files, giving raise to a network structure, by graph-based searches. Full article
(This article belongs to the Section Network)
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Open AccessArticle An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning
Mach. Learn. Knowl. Extr. 2018, 1(1), 192-204; https://doi.org/10.3390/make1010011
Received: 4 September 2018 / Revised: 28 October 2018 / Accepted: 7 November 2018 / Published: 9 November 2018
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Abstract
Today, the main two security issues for deep learning are data poisoning and adversarial examples. Data poisoning consists of perverting a learning system by manipulating a small subset of the training data, while adversarial examples entail bypassing the system at testing time with
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Today, the main two security issues for deep learning are data poisoning and adversarial examples. Data poisoning consists of perverting a learning system by manipulating a small subset of the training data, while adversarial examples entail bypassing the system at testing time with low-amplitude manipulation of the testing sample. Unfortunately, data poisoning that is invisible to human eyes can be generated by adding adversarial noise to the training data. The main contribution of this paper includes a successful implementation of such invisible data poisoning using image classification datasets for a deep learning pipeline. This implementation leads to significant classification accuracy gaps. Full article
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Open AccessReview Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives
Mach. Learn. Knowl. Extr. 2018, 1(1), 157-191; https://doi.org/10.3390/make1010010
Received: 1 September 2018 / Revised: 3 October 2018 / Accepted: 4 October 2018 / Published: 10 October 2018
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Abstract
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer
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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader. Full article
Open AccessViewpoint A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition
Mach. Learn. Knowl. Extr. 2018, 1(1), 149-156; https://doi.org/10.3390/make1010009
Received: 28 July 2018 / Revised: 24 August 2018 / Accepted: 4 September 2018 / Published: 8 September 2018
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Abstract
Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its
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Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles. Full article
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Open AccessPerspective Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?
Mach. Learn. Knowl. Extr. 2018, 1(1), 138-148; https://doi.org/10.3390/make1010008
Received: 27 July 2018 / Revised: 17 August 2018 / Accepted: 20 August 2018 / Published: 22 August 2018
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Abstract
Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As
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Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike. Full article
(This article belongs to the Section Network)
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Open AccessArticle Phi-Delta-Diagrams: Software Implementation of a Visual Tool for Assessing Classifier and Feature Performance
Mach. Learn. Knowl. Extr. 2018, 1(1), 121-137; https://doi.org/10.3390/make1010007
Received: 15 May 2018 / Revised: 22 June 2018 / Accepted: 27 June 2018 / Published: 28 June 2018
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Abstract
In this article, a two-tiered 2D tool is described, called φ,δ diagrams, and this tool has been devised to support the assessment of classifiers in terms of accuracy and bias. In their standard versions, these diagrams provide information, as
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In this article, a two-tiered 2D tool is described, called φ,δ diagrams, and this tool has been devised to support the assessment of classifiers in terms of accuracy and bias. In their standard versions, these diagrams provide information, as the underlying data were in fact balanced. Their generalization, i.e., ability to account for the imbalance, will be also briefly described. In either case, the isometrics of accuracy and bias are immediately evident therein, as—according to a specific design choice—they are in fact straight lines parallel to the x-axis and y-axis, respectively. φ,δ diagrams can also be used to assess the importance of features, as highly discriminant ones are immediately evident therein. In this paper, a comprehensive introduction on how to adopt φ,δ diagrams as a standard tool for classifier and feature assessment is given. In particular, with the goal of illustrating all relevant details from a pragmatic perspective, their implementation and usage as Python and R packages will be described. Full article
(This article belongs to the Section Visualization)
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Open AccessReview Why Topology for Machine Learning and Knowledge Extraction?
Mach. Learn. Knowl. Extr. 2018, 1(1), 115-120; https://doi.org/10.3390/make1010006
Received: 10 March 2018 / Revised: 26 April 2018 / Accepted: 30 April 2018 / Published: 2 May 2018
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Abstract
Data has shape, and shape is the domain of geometry and in particular of its “free” part, called topology. The aim of this paper is twofold. First, it provides a brief overview of applications of topology to machine learning and knowledge extraction, as
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Data has shape, and shape is the domain of geometry and in particular of its “free” part, called topology. The aim of this paper is twofold. First, it provides a brief overview of applications of topology to machine learning and knowledge extraction, as well as the motivations thereof. Furthermore, this paper is aimed at promoting cross-talk between the theoretical and applied domains of topology and machine learning research. Such interactions can be beneficial for both the generation of novel theoretical tools and finding cutting-edge practical applications. Full article
(This article belongs to the Section Topology)
Open AccessArticle A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks
Mach. Learn. Knowl. Extr. 2018, 1(1), 75-114; https://doi.org/10.3390/make1010005
Received: 15 March 2018 / Revised: 16 April 2018 / Accepted: 26 April 2018 / Published: 30 April 2018
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Abstract
As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based accelerators has grown significantly. Resistive random access memory (ReRAM) is a
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As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based accelerators has grown significantly. Resistive random access memory (ReRAM) is a promising technology for efficiently architecting PIM- and NN-based accelerators due to its capabilities to work as both: High-density/low-energy storage and in-memory computation/search engine. In this paper, we present a survey of techniques for designing ReRAM-based PIM and NN architectures. By classifying the techniques based on key parameters, we underscore their similarities and differences. This paper will be valuable for computer architects, chip designers and researchers in the area of machine learning. Full article
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Open AccessArticle A Machine Learning Approach to Determine Oyster Vessel Behavior
Mach. Learn. Knowl. Extr. 2018, 1(1), 64-74; https://doi.org/10.3390/make1010004
Received: 14 December 2017 / Revised: 20 March 2018 / Accepted: 29 March 2018 / Published: 31 March 2018
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Abstract
In this work, we address a multi-class classification task of oyster vessel behaviors determination by classifying them into four different classes: fishing, traveling, poling (exploring) and docked (anchored). The main purpose of this work is to automate the oyster vessel behaviors determination task
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In this work, we address a multi-class classification task of oyster vessel behaviors determination by classifying them into four different classes: fishing, traveling, poling (exploring) and docked (anchored). The main purpose of this work is to automate the oyster vessel behaviors determination task using machine learning and to explore different techniques to improve the accuracy of the oyster vessel behavior prediction problem. To employ machine learning technique, two important descriptors: speed and net speed, are calculated from the trajectory data, recorded by a satellite communication system (Vessel Management System, VMS) attached to the vessels fishing on the public oyster grounds of Louisiana. We constructed a support vector machine (SVM) based method which employs Radial Basis Function (RBF) as a kernel to accurately predict the behavior of oyster vessels. Several validation and parameter optimization techniques were used to improve the accuracy of the SVM classifier. A total 93% of the trajectory data from a July 2013 to August 2014 dataset consisting of 612,700 samples for which the ground truth can be obtained using rule-based classifier is used for validation and independent testing of our method. The results show that the proposed SVM based method is able to correctly classify 99.99% of 612,700 samples using the 10-fold cross validation. Furthermore, we achieved a precision of 1.00, recall of 1.00, F1-score of 1.00 and a test accuracy of 99.99%, while performing an independent test using a subset of 93% of the dataset, which consists of 31,418 points. Full article
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Open AccessArticle Category Maps Describe Driving Episodes Recorded with Event Data Recorders
Mach. Learn. Knowl. Extr. 2018, 1(1), 43-63; https://doi.org/10.3390/make1010003
Received: 29 January 2018 / Revised: 7 March 2018 / Accepted: 8 March 2018 / Published: 12 March 2018
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Abstract
This study was conducted to create driving episodes using machine-learning-based algorithms that address long-term memory (LTM) and topological mapping. This paper presents a novel episodic memory model for driving safety according to traffic scenes. The model incorporates three important features: adaptive resonance theory
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This study was conducted to create driving episodes using machine-learning-based algorithms that address long-term memory (LTM) and topological mapping. This paper presents a novel episodic memory model for driving safety according to traffic scenes. The model incorporates three important features: adaptive resonance theory (ART), which learns time-series features incrementally while maintaining stability and plasticity; self-organizing maps (SOMs), which represent input data as a map with topological relations using self-mapping characteristics; and counter propagation networks (CPNs), which label category maps using input features and counter signals. Category maps represent driving episode information that includes driving contexts and facial expressions. The bursting states of respective maps produce LTM created on ART as episodic memory. For a preliminary experiment using a driving simulator (DS), we measure gazes and face orientations of drivers as their internal information to create driving episodes. Moreover, we measure cognitive distraction according to effects on facial features shown in reaction to simulated near-misses. Evaluation of the experimentally obtained results show the possibility of using recorded driving episodes with image datasets obtained using an event data recorder (EDR) with two cameras. Using category maps, we visualize driving features according to driving scenes on a public road and an expressway. Full article
(This article belongs to the Section Learning)
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Open AccessArticle Learning to Teach Reinforcement Learning Agents
Mach. Learn. Knowl. Extr. 2018, 1(1), 21-42; https://doi.org/10.3390/make1010002
Received: 19 September 2017 / Revised: 17 November 2017 / Accepted: 1 December 2017 / Published: 6 December 2017
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Abstract
In this article, we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we examine several critical factors
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In this article, we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we examine several critical factors affecting advice quality in this setting, such as the average performance of the teacher, its variance and the importance of reward discounting in advising. The experiments show that the best performers are not always the best teachers and reveal the non-trivial importance of the coefficient of variation (CV) as a statistic for choosing policies that generate advice. The CV statistic relates variance to the corresponding mean. Second, the article studies policy learning for distributing advice under a budget. Whereas most methods in the relevant literature rely on heuristics for advice distribution, we formulate the problem as a learning one and propose a novel reinforcement learning algorithm capable of learning when to advise or not. The proposed algorithm is able to advise even when it does not have knowledge of the student’s intended action and needs significantly less training time compared to previous learning approaches. Finally, in this article, we argue that learning to advise under a budget is an instance of a more generic learning problem: Constrained Exploitation Reinforcement Learning. Full article
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Open AccessEditorial Introduction to MAchine Learning & Knowledge Extraction (MAKE)
Mach. Learn. Knowl. Extr. 2018, 1(1), 1-20; https://doi.org/10.3390/make1010001
Received: 8 May 2017 / Revised: 18 June 2017 / Accepted: 23 June 2017 / Published: 3 July 2017
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Abstract
The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to
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The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community. Full article
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