850 research outputs found
Spanish question answering evaluation
This paper reports the most significant issues related to the launching of a Monolingual Spanish Question Answering evaluation track at the Cross Language Evaluation Forum (CLEF 2003). It introduces some questions about multilingualism and describes the methodology for test suite production, task, judgment of answers as well as the results obtained by the participant systems
Learning Multi-label Alternating Decision Trees from Texts and Data
International audienceMulti-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer?s Adaboost.MH and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data. Keywords boosting - alternating decision trees - text mining - multi-label problem
A Non-Sequential Representation of Sequential Data for Churn Prediction
We investigate the length of event sequence giving best predictions
when using a continuous HMM approach to churn prediction from sequential
data. Motivated by observations that predictions based on only the few most recent
events seem to be the most accurate, a non-sequential dataset is constructed
from customer event histories by averaging features of the last few events. A simple
K-nearest neighbor algorithm on this dataset is found to give significantly
improved performance. It is quite intuitive to think that most people will react
only to events in the fairly recent past. Events related to telecommunications occurring
months or years ago are unlikely to have a large impact on a customer’s
future behaviour, and these results bear this out. Methods that deal with sequential
data also tend to be much more complex than those dealing with simple nontemporal
data, giving an added benefit to expressing the recent information in a
non-sequential manner
Tight Combinatorial Generalization Bounds for Threshold Conjunction Rules
Abstract. We propose a combinatorial technique for obtaining tight data dependent generalization bounds based on a splitting and connec-tivity graph (SC-graph) of the set of classifiers. We apply this approach to a parametric set of conjunctive rules and propose an algorithm for effective SC-bound computation. Experiments on 6 data sets from the UCI ML Repository show that SC-bound helps to learn more reliable rule-based classifiers as compositions of less overfitted rules
An artificial immune system for fuzzy-rule induction in data mining
This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units
We present ARU, an Adaptive Recurrent Unit for streaming adaptation of deep
globally trained time-series forecasting models. The ARU combines the
advantages of learning complex data transformations across multiple time series
from deep global models, with per-series localization offered by closed-form
linear models. Unlike existing methods of adaptation that are either
memory-intensive or non-responsive after training, ARUs require only fixed
sized state and adapt to streaming data via an easy RNN-like update operation.
The core principle driving ARU is simple --- maintain sufficient statistics of
conditional Gaussian distributions and use them to compute local parameters in
closed form. Our contribution is in embedding such local linear models in
globally trained deep models while allowing end-to-end training on the one
hand, and easy RNN-like updates on the other. Across several datasets we show
that ARU is more effective than recently proposed local adaptation methods that
tax the global network to compute local parameters.Comment: 9 pages, 4 figure
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