2,114 research outputs found
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Networks (EDEN), a computationally efficient
neuro-evolutionary algorithm which interfaces to any deep neural network
platform, such as TensorFlow. We show that EDEN evolves simple yet successful
architectures built from embedding, 1D and 2D convolutional, max pooling and
fully connected layers along with their hyperparameters. Evaluation of EDEN
across seven image and sentiment classification datasets shows that it reliably
finds good networks -- and in three cases achieves state-of-the-art results --
even on a single GPU, in just 6-24 hours. Our study provides a first attempt at
applying neuro-evolution to the creation of 1D convolutional networks for
sentiment analysis including the optimisation of the embedding layer.Comment: 7 pages, 3 figures, 3 tables and see video
https://vimeo.com/23451009
Evolving text classification rules with genetic programming
We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications
Text document pre-processing using the Bayes formula for classification based on the vector space model
This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naĂŻve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data
Text document pre-processing using the Bayes formula for classification based on the vector space model
This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naĂŻve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
Bagging and boosting classification trees to predict churn.
Bagging; Boosting; Classification; Churn;
Confronting Student Prejudice with "Mario Kart" Nintendo Wii*
This paper explores the use of “Mario Kart” Nintendo Wii as an active learning tool to teach about intergroup conflict as a cause of prejudice. Participating students were randomly placed
into two “Mario Kart” Nintendo Wii tournaments. Students then competed as team members and ranked personality traits for their team (in-Âgroup) and the opposing team (out-Âgroup). Discussion of the results focused on the role of competition
in creating in-Âgroup/out-Âgroup biases and how this relates to prejudice. Results from a pre-Âtest/post-Âtest quiz indicated that students understood these concepts more clearly after the tournaments were held. Furthermore, those who participated improved their scores more than those who did not articipate
in the tournaments
An Eye for an Eye: Impact of Sequelization and Comparison in Advertisements on Consumer’s Perception of Brands
In this paper we demonstrate that the positive effects of comparative advertising are significantly diluted when a compared-to brand retaliates. Retaliation introduces sequencing in advertisements. We therefore evaluate sequelized advertisements (both comparative and noncomparative) alongside comparative advertisements and ordinary advertisements. We show that, given no threat of comparative advertising from competitors, sequelizing a popular advertisement may be as potent as comparative advertising, in terms of improving consumers’ recall as well as preference for the sponsored brand. Furthermore, an advertisement message may be directed at core benefits (and/or attributes) that a brand promises, or at a stylized theme or storyline that use peripheral cues to indirectly convey the brand’s deliverables. We incorporate this dimension of communication focus and conclude that while comparative advertisements are more effective with objective messages, noncomparative sequelized advertisements work better with thematic or story based messages.
Bagging and boosting classification trees to predict churn.
In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a significant increase of the classification performance when applied to the customer database. Furthermore, we compare bagged and boosted classifiers computed, respectively, from a balanced versus a proportional sample to predict a rare event (here, churn), and propose a simple correction method for classifiers constructed from balanced training samples.Algorithms; Bagging; Boosting; Churn; Classification; Classifiers; Companies; Data; Gini coefficient; Methods; Performance; Rare events; Sampling; Top decile; Training;
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