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Lazy Associative Classification
TLDR
This paper demonstrates that an associative classifier performs no worse than the corresponding decision tree classifier, and demonstrates that lazy classifiers outperform the corresponding eager ones.
Dengue surveillance based on a computational model of spatio-temporal locality of Twitter
TLDR
This paper analyzes how Dengue epidemic is reflected on Twitter and to what extent that information can be used for the sake of surveillance and proposes an active surveillance methodology based on four dimensions: volume, location, time and public perception.
Reverse engineering socialbot infiltration strategies in Twitter
TLDR
This analysis is the first of a kind, and reveals what strategies make socialbots successful in the Twitter-sphere, and employs a 2k factorial design experiment to quantify the infiltration effectiveness of different socialbot strategies.
Effective self-training author name disambiguation in scholarly digital libraries
TLDR
A novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that eliminates the need of any manual labeling effort and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.
Pareto-efficient hybridization for multi-objective recommender systems
TLDR
A hybrid recommendation approach that combines existing algorithms which differ in their level of accuracy, novelty and diversity, and allows for adjusting the compromise between accuracy, diversity and novelty, so that the recommendation emphasis can be adjusted dynamically according to the needs of different users.
Supervised Learning for Fake News Detection
TLDR
A new set of features is presented and the prediction performance of current approaches and features for automatic detection of fake news are measured, revealing interesting findings on the usefulness and importance of features for detecting false news.
From bias to opinion: a transfer-learning approach to real-time sentiment analysis
TLDR
This paper adopted user bias as the basis for building accurate classification models and applied its model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League.
Multiobjective Pareto-Efficient Approaches for Recommender Systems
TLDR
The proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse, and novel and discussed scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy.
Active Learning Genetic programming for record deduplication
TLDR
This paper presents the Active Learning GP (AGP), a semi-supervised GP, and instantiates it for the data deduplication problem, using an active learning approach in which a committee of multi-attribute functions votes for classifying record pairs as duplicates or not.
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