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Empirical Approach to Machine Learning
ویرایش: نویسندگان: Plamen P. Angelov, Xiaowei Gu سری: Studies in Computational Intelligence 800 ISBN (شابک) : 9783030023836 ناشر: Springer سال نشر: 2019 تعداد صفحات: 437 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 21 مگابایت
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توضیحاتی درمورد کتاب به خارجی
This book is a product of focused research over the last several years and brings in one place the fundamentals of a new methodological approach to machine learning that is centered entirely at the actual data. We call this approach “empirical” to distinguish it from the traditional approach that is heavily restricted and driven by the prior assumptions about data distribution and data generation model. This new approach is not only liberating (no need to make prior assumptions about the type of data distribution, amount of the data and even their nature—random or deterministic), but it is also fundamentally different—it places the mutual position of each data sample in the data space at the center of the analysis. It is also closely related to the concepts of data density and has close resemblance to centrality (known from the network theory) and inverse square distance rule/law (known form physics/astronomy). Furthermore, this approach has anthropomorphic characteristics. For example, unlike the vast majority of the existing machine learning methods which require a large amount of training data the proposed approach allows to learn from a handful or even a single example, that is to start “from scratch” and also to learn continuously even after training/deployment. That is, the machine can learn lifelong or continuously without or with very little human intervention or supervision. Critically, the proposed approach is not “black box” unlike many of the existing competitors, e.g., most of the neural networks (NN), the celebrated deep learning, etc. On the contrary, it is fully interpretable, transparent, has a clear and logical internal model structure, can carry semantic meaning and is, thus, much more human-like. Traditional machine learning is statistical and is based on the classical probability theory which guarantees, due to its solid mathematical foundation, the properties of these learning algorithms when the amount of the data tends to infinity and all the data come from the same distribution. Nonetheless, the presumed random nature and same distribution imposed on the data generation model are too strong and impractical to be held true in real situations. In addition, the predefined parameters of machine learning algorithms usually require a certain amount of prior knowledge of the problem, which, in reality, is often unavailable. Thus, these parameters are impossible to be correctly defined in real applications, and the performance of the algorithms can be largely influenced by the improper choice. Importantly, even though the newly proposed concept is centered at experimental data, it leads to a theoretically sound closed-form model of the data distribution and has theoretically proven convergence (in the mean), stability, and local optimality. Despite the seeming similarity of the end result to the traditional approach, this data distribution is extracted from the data and not assumed a priori. This new quantity that represents the likelihood also integrates to 1 and is represented as a continuous function; however, unlike the traditional pdf (probability density function), it does not suffer from obvious paradoxes. We call this new quantity “typicality”. We also introduce another new quantity, called “eccentricity” which is inverse of the data density and is very convenient for the analysis of the anomalies/outliers/faults simplifying the Chebyshev inequality expression and analysis. Eccentricity is a new measure of the tail of the distribution, which is introduced for the first time by the lead author in his recent previous works. Based on the typicality (used instead of the probability density function) and eccentricity as new measures derived directly and entirely from the experimental data, we develop a new methodological basis for data analysis called empirical. We also redefine and simplify the fuzzy sets and systems definition. Traditionally, fuzzy sets are defined through their membership functions. This is often a problem because to define a suitable membership function may not be easy or convenient and is based on prior assumptions and approximations. Instead, we propose to only select prototypes. These prototypes can be actual data samples selected autonomously due to their high descriptive/representative power (having high local typicality and density) or pointed by an expert (for the fuzzy sets the role of the human expert and user is desirable and natural). Even when these prototypes are identified by the human expert and not autonomously from the experimental data, the benefit is significant because the cumbersome, possibly prohibitive and potentially controversial problem of defining potentially a huge number of membership functions, can be circumvented and tremendously simplified. Based on the new, empirical (based on the actual/real data and not on the assumptions made about the data generation model) methodology, we further analyze and redefine the main elements of the machine learning/pattern recognition/ data mining, deep learning as well as anomaly detection, fault detection and identification. We start with the data pre-processing and anomaly detection. This problem is the basis of multiple and various applications to fault detection in engineering systems, intruder and insider detection in cybersecurity systems, outlier detection in data mining, etc. Eccentricity is a new, more convenient form for analysis of such properties of the data. Data density and, especially, its recursive form of update, which we call RDE (recursive density estimation), makes the analysis of anomalies very convenient, as it will be illustrated in the book. We further introduce a new method for fully autonomous (and, thus, not based on handcrafting, selecting thresholds, parameters, and coefficients by the user or “tailoring” these to the problem) data partitioning. In essence, this is a new method for clustering, which is, however, fully data-driven. It combines rank-ordering (in terms of data density) with the distance between any point and the point with the maximum typicality. We also introduce a number of autonomous clustering methods (online, evolving, taking into account local anomalies, etc.) and compare these with the currently existing alternatives. In this sense, this book builds upon the previous research monograph by the lead author entitled Autonomous Learning Systems: From Data Streams to Knowledge in Real time, Willey, 2012, ISBN 978-1-119-95152-0. We then move to supervised learning starting with the classifiers. We focus on fuzzy rule-based (FRB) systems as classifiers, but it is important to stress that since FRBs and artificial neural networks (ANN) were demonstrated to be dual (the term neuro-fuzzy is widely used to indicate their close relation), everything presented in this book can also be interpreted as NNs. Using FRB and, respectively, ANNs as classifiers is not a new concept. In this book, we introduce the interpretable deep rule-based (DRB) classifiers as a new powerful form of machine learning, specifically effective for image classification, which has anthropomorphic characteristics as described earlier. The importance of DRB is multifaceted. It concerns not only the efficiency (very low training time, low computing resources required—no graphic processing units (GPU), for example), high precision (classification rate) competing, and surpassing the best published results and the human abilities, but also high interpretability/transparency, repeatability, proven convergence, optimality, non-parametric, non-iterative nature, and self-evolving capability. This new method is compared thoroughly with the best existing alternatives. It can start learning from the very first image presented (very much like humans can). The DRB method can be considered as neuro-fuzzy. We pioneer the deep FRB as highly parallel multi-layer classifiers which offer the high interpretability/ transparency typical for the FRB. Indeed, up until now the so-called deep learning method proved its efficiency and high potential as a type of artificial/computational NN, but it was not combined with fuzzy rules to benefit from their semantic clarity. Another important supervised learning constructs are the predictive models, which can be of regression or time series type. These are traditionally being approached in the same way—starting with the prior assumptions about inputs/ features, cause-effect relations, data generation model, and density distributions and the actual experimental data are only used to confirm or correct these assumptions made a priori. The proposed empirical approach, on the contrary, starts with the data and their mutual position in the data space and extracts all internal dependencies in a convenient form from these. It self-evolves from data complex non-linear predictive models. These can be interpreted as IF…THEN FRB of a particular type, called AnYa or, equally, as self-evolving computational ANN. In this sense, this book builds upon the first research monograph by the lead author entitled Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems, Springer, 2002, ISBN 978-3-7908-1794-2. In this book, we use the fully autonomous data partitioning (ADP) method introduced in earlier chapters to form the model structure (the premise/IF part). These are the local peaks (modes) of the multi-modal (mountain-like) typicality distribution, which is automatically extracted from the actual/observable data. In this book, we offer locally optimal method for ADP (satisfying Karush-Kuhn-Tucker conditions). The consequent/THEN part of the self-evolving FRB based predictive models is linear and fuzzily weighted. In this book, we provide theoretical proof of the convergence of the error (in the mean) using Lyapunov functions. In this way, this presents the first FRB with self-evolving nature with theoretically proven convergence (in the mean) in training (including online, during the use), stability as well as local optimality of the premise (IF) part of the model structure. The properties of local optimality, convergence, and stability are illustrated on a set of benchmark experimental data sets and streams. Last, but not least, the authors would like to express their gratitude for the close collaboration on some aspects of this new concept with Prof. Jose Principe (University of Florida, USA, in the framework of The Royal society grant “Novel Machine Learning Methods for Big Data Streams”), Dr. Dmitry Kangin (former Ph.D. student at Lancaster University with the lead author, currently Postdoc Researcher at Exeter University, UK), Dr. Bruno Sielly Jales Costa (visiting Ph.D. student at Lancaster University with the lead author, now with Ford R&D, Palo Alto, USA), Dr. Dimitar Filev (former PhD advisor of the lead author in early 1990s, now Henry Ford Technical Fellow at Ford R&D, Dearborn, MI, USA), and Prof. Ronald Yager (Iona College, NY, USA), Dr. Hai-Jun Rong (visiting scholar at Lancaster University with the lead author, Associate Professor at Xi’an Jiaotong University, Xi’an, China).
فهرست مطالب
Front Matter ....Pages i-xxix
Introduction (Plamen P. Angelov, Xiaowei Gu)....Pages 1-14
Front Matter ....Pages 15-15
Brief Introduction to Statistical Machine Learning (Plamen P. Angelov, Xiaowei Gu)....Pages 17-67
Brief Introduction to Computational Intelligence (Plamen P. Angelov, Xiaowei Gu)....Pages 69-99
Front Matter ....Pages 101-101
Empirical Approach—Introduction (Plamen P. Angelov, Xiaowei Gu)....Pages 103-133
Empirical Fuzzy Sets and Systems (Plamen P. Angelov, Xiaowei Gu)....Pages 135-155
Anomaly Detection—Empirical Approach (Plamen P. Angelov, Xiaowei Gu)....Pages 157-173
Data Partitioning—Empirical Approach (Plamen P. Angelov, Xiaowei Gu)....Pages 175-198
Autonomous Learning Multi-model Systems (Plamen P. Angelov, Xiaowei Gu)....Pages 199-222
Transparent Deep Rule-Based Classifiers (Plamen P. Angelov, Xiaowei Gu)....Pages 223-245
Front Matter ....Pages 247-247
Applications of Autonomous Anomaly Detection (Plamen P. Angelov, Xiaowei Gu)....Pages 249-259
Applications of Autonomous Data Partitioning (Plamen P. Angelov, Xiaowei Gu)....Pages 261-276
Applications of Autonomous Learning Multi-model Systems (Plamen P. Angelov, Xiaowei Gu)....Pages 277-293
Applications of Deep Rule-Based Classifiers (Plamen P. Angelov, Xiaowei Gu)....Pages 295-319
Applications of Semi-supervised Deep Rule-Based Classifiers (Plamen P. Angelov, Xiaowei Gu)....Pages 321-340
Epilogue (Plamen P. Angelov, Xiaowei Gu)....Pages 341-346
Back Matter ....Pages 347-423
