Ioannis Katakis

Bio: Ioannis Katakis is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Sentiment analysis & Voting. The author has an hindex of 17, co-authored 49 publications receiving 5465 citations. Previous affiliations of Ioannis Katakis include Aristotle University of Thessaloniki & University of Cyprus.

An adaptive personalized news dissemination system

Abstract: With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW ( http://news.csd.auth.gr ).

Detecting Events in Online Social Networks: Definitions, Trends and Challenges

Abstract: Event detection is a research area that attracted attention during the last years due to the widespread availability of social media data. The problem of event detection has been examined in multiple social media sources like Twitter, Flickr, YouTube and Facebook. The task comprises many challenges including the processing of large volumes of data and high levels of noise. In this article, we present a wide range of event detection algorithms, architectures and evaluation methodologies. In addition, we extensively discuss on available datasets, potential applications and open research issues. The main objective is to provide a compact representation of the recent developments in the field and aid the reader in understanding the main challenges tackled so far as well as identifying interesting future research directions.

Effective voting of heterogeneous classifiers

Abstract: This paper deals with the combination of classification models that have been derived from running different (heterogeneous) learning algorithms on the same data set. We focus on the Classifier Evaluation and Selection (ES) method, that evaluates each of the models (typically using 10-fold cross-validation) and selects the best one. We examine the performance of this method in comparison with the Oracle selecting the best classifier for the test set and show that 10-fold cross-validation has problems in detecting the best classifier. We then extend ES by applying a statistical test to the 10-fold accuracies of the models and combining through voting the most significant ones. Experimental results show that the proposed method, Effective Voting, performs comparably with the state-of-the-art method of Stacking with Multi-Response Model Trees without the additional computational cost of meta-training.

On the utility of incremental feature selection for the classification of textual data streams

Abstract: In this paper we argue that incrementally updating the features that a text classification algorithm considers is very important for real-world textual data streams, because in most applications the distribution of data and the description of the classification concept changes over time. We propose the coupling of an incremental feature ranking method and an incremental learning algorithm that can consider different subsets of the feature vector during prediction (what we call a feature based classifier), in order to deal with the above problem. Experimental results with a longitudinal database of real spam and legitimate emails shows that our approach can adapt to the changing nature of streaming data and works much better than classical incremental learning algorithms.

An Ensemble of Classifiers for coping with Recurring Contexts in Data Streams

Abstract: This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identify recurring contexts. The ensemble is produced by maintaining an classifier for every concept discovered in the stream The full version of this paper as well as the datasets used for evaluation can be found at: http://mlkd.csd.auth.gr/concept_drift.html

node2vec: Scalable Feature Learning for Networks

Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

A Review On Multi-Label Learning Algorithms

Abstract: Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.