2,006 research outputs found
Dynamic Adaptation on Non-Stationary Visual Domains
Domain adaptation aims to learn models on a supervised source domain that
perform well on an unsupervised target. Prior work has examined domain
adaptation in the context of stationary domain shifts, i.e. static data sets.
However, with large-scale or dynamic data sources, data from a defined domain
is not usually available all at once. For instance, in a streaming data
scenario, dataset statistics effectively become a function of time. We
introduce a framework for adaptation over non-stationary distribution shifts
applicable to large-scale and streaming data scenarios. The model is adapted
sequentially over incoming unsupervised streaming data batches. This enables
improvements over several batches without the need for any additionally
annotated data. To demonstrate the effectiveness of our proposed framework, we
modify associative domain adaptation to work well on source and target data
batches with unequal class distributions. We apply our method to several
adaptation benchmark datasets for classification and show improved classifier
accuracy not only for the currently adapted batch, but also when applied on
future stream batches. Furthermore, we show the applicability of our
associative learning modifications to semantic segmentation, where we achieve
competitive results
An Efficient Hybrid Classifier Model for Customer Churn Prediction
Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall
A modified multi-class association rule for text mining
Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of rules produced in order to obtain high classification accuracy. This study proposes s a Modified Multi-class Association Rule Mining (mMCAR) that consists of three procedures; rule discovery, rule pruning and group-based class assignment. The rule discovery and rule pruning
procedures are designed to reduce the number of classification rules. On the other hand, the group-based class assignment procedure contributes in improving the classification accuracy. Experiments on the structured and unstructured text datasets
obtained from the UCI and Reuters repositories are performed in order to evaluate the proposed Association Classifier. The proposed mMCAR classifier is benchmarked against the traditional classifiers and existing Association Classifiers.
Experimental results indicate that the proposed Association Classifier, mMCAR, produced high accuracy with a smaller number of classification rules. For the structured dataset, the mMCAR produces an average of 84.24% accuracy as compared to MCAR that obtains 84.23%. Even though the classification accuracy difference is small, the proposed mMCAR uses only 50 rules for the classification while its benchmark method involves 60 rules. On the other hand, mMCAR is at par
with MCAR when unstructured dataset is utilized. Both classifiers produce 89% accuracy but mMCAR uses less number of rules for the classification. This study contributes to the text mining domain as automatic classification of huge and widely
distributed textual data could facilitate the text representation and retrieval processes
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