Fast Construction of Relational Features for Machine Learning

Katedra kybernetik

Adaptive algorithms for real-world transactional data mining.

The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling

Learning Collective Behavior in Multi-relational Networks

With the rapid expansion of the Internet and WWW, the problem of analyzing social media data has received an increasing amount of attention in the past decade. The boom in social media platforms offers many possibilities to study human collective behavior and interactions on an unprecedented scale. In the past, much work has been done on the problem of learning from networked data with homogeneous topologies, where instances are explicitly or implicitly inter-connected by a single type of relationship. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, networked data extracted from social media, web pages, and bibliographic databases can contain entities of multiple classes and linked by various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. Learning the collective behavior and interactions in heterogeneous networks becomes much more complex. The contribution of this dissertation include 1) two classification frameworks for identifying human collective behavior in multi-relational social networks; 2) unsupervised and supervised learning models for relationship prediction in multi-relational collaborative networks. Our methods improve the performance of homogeneous predictive models by differentiating heterogeneous relations and capturing the prominent interaction patterns underlying the network structure. The work has been evaluated in various real-world social networks. We believe that this study will be useful for analyzing human collective behavior and interactions specifically in the scenario when the heterogeneous relationships in the network arise from various causal reasons

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence

Semantic Feature Construction

An effective set of features is integral to the success of machine learning algorithms. Semantic feature construction is the knowledge-driven manipulation of the propositional descriptor space of a set of examples for use in a learning algorithm. Two important sources of semanticsfor feature construction are the semantic type (and associated semantic properties) and the semantic class of features. These semantics canbe captured in a knowledge base and utilized to constrain search through the space of constructed features. This dissertation presents a systemthat captures semantic feature construction knowledge and implements a search algorithm that respects that knowledge. Results are presentedfor different combinations of features generated from different successor functions used in search. These results are compiled over many learning problems and several learning algorithms. Other results are also presentedfor different levels of detail in semantic knowledge. Generally, semantics are an effective guide in the space of constructed features