Joao Gama

Universidade do Porto, Liaad, Faculty Member

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Address: Porto, Porto, Portugal

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Stefano Maffei

Politecnico di Milano

Armando Marques-Guedes

UNL - New University of Lisbon

Gautam Biswas

Vanderbilt University

Ömer Faruk Eker

Cranfield University

Adolfo Vasquez Rocca

Universidad Complutense de Madrid

Bogdan Gabrys

University of Technology Sydney

Leonard Bond

Iowa State University

Daniel Martin Feige

Akademie der Bildenden Künste Stuttgart

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RIVERS STATE POLYTECHNIC

Wenceslao Palma

Pontificia Universidad Catolica de Valparaiso

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Papers by Joao Gama

Machine Learning

IGI Global eBooks, May 24, 2011

Machine learning techniques have been successfully applied to several real world problems in area... more Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

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A machine learning approach that is capable of treating data streams presents new challenges and ... more A machine learning approach that is capable of treating data streams presents new challenges and enables the analysis of a variety of real problems in which concepts change over time. In this scenario, the ability to identify novel concepts as well as to deal with concept drift are two important attributes. This paper presents a technique based on the k-means

Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks

In this paper, a cluster-based novelty detection technique capable of dealing with a large amount... more In this paper, a cluster-based novelty detection technique capable of dealing with a large amount of data is presented and evaluated in the context of intrusion detection. Starting with examples of a single class that describe the normal profile, the proposed technique detects novel concepts initially as cohesive clusters of examples and later as sets of clusters in an unsupervised

Anomaly Detection in Sequential Data: Principles and Case Studies

Wiley Encyclopedia of Electrical and Electronics Engineering

Forecasting Financial Market Structure from Network Features using Machine Learning

Research Square (Research Square), 2021

We propose a model that forecasts market correlation structure from link-and node-based financial... more We propose a model that forecasts market correlation structure from link-and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to 40% improvement over a time-invariant correlation-based benchmark. Non pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and 1 2 Forecasting Financial Market Structure using Machine Learning NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.

Joao Gama

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