1,064 research outputs found
Ensemble pruning for text categorization based on data partitioning
Ensemble methods can improve the effectiveness in text categorization. Due to computation cost of ensemble approaches there is a need for pruning ensembles. In this work we study ensemble pruning based on data partitioning. We use a ranked-based pruning approach. For this purpose base classifiers are ranked and pruned according to their accuracies in a separate validation set. We employ four data partitioning methods with four machine learning categorization algorithms. We mainly aim to examine ensemble pruning in text categorization. We conduct experiments on two text collections: Reuters-21578 and BilCat-TRT. We show that we can prune 90% of ensemble members with almost no decrease in accuracy. We demonstrate that it is possible to increase accuracy of traditional ensembling with ensemble pruning. © 2011 Springer-Verlag Berlin Heidelberg
Text categorization and ensemble pruning in Turkish news portals
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 53-60.In news portals, text category information is needed for news presentation. However,
for many news stories the category information is unavailable, incorrectly
assigned or too generic. This makes the text categorization a necessary tool
for news portals. Automated text categorization (ATC) is a multifaceted diffi-
cult process that involves decisions regarding tuning of several parameters, term
weighting, word stemming, word stopping, and feature selection. It is important
to find a categorization setup that will provide highly accurate results in ATC for
Turkish news portals. Two Turkish test collections with different characteristics
are created using Bilkent News Portal. Experiments are conducted with four classification
methods: C4.5, KNN, Naive Bayes, and SVM (using polynomial and
rbf kernels). Results recommend a text categorization template for Turkish news
portals. Regarding recommended text categorization template, ensemble learning
methods are applied to increase effectiveness. Since they require many computational
workload, ensemble pruning strategies are developed. Data partitioning
ensembles are constructed and ranked-based ensemble pruning is applied with
several machine learning categorization algorithms. The aim is to answer the following
questions: (1) How much data can we prune using data partitioning on the
text categorization domain? (2) Which partitioning and categorization methods
are more suitable for ensemble pruning? (3) How do English and Turkish differ
in ensemble pruning? (4) Can we increase effectiveness with ensemble pruning
in the text categorization? Experiments are conducted on two text collections:
Reuters-21578 and BilCat-TRT. 90% of ensemble members can be pruned with
almost no decreasing in accuracy.Toraman, ÇağrıM.S
Asynchronous Training of Word Embeddings for Large Text Corpora
Word embeddings are a powerful approach for analyzing language and have been
widely popular in numerous tasks in information retrieval and text mining.
Training embeddings over huge corpora is computationally expensive because the
input is typically sequentially processed and parameters are synchronously
updated. Distributed architectures for asynchronous training that have been
proposed either focus on scaling vocabulary sizes and dimensionality or suffer
from expensive synchronization latencies.
In this paper, we propose a scalable approach to train word embeddings by
partitioning the input space instead in order to scale to massive text corpora
while not sacrificing the performance of the embeddings. Our training procedure
does not involve any parameter synchronization except a final sub-model merge
phase that typically executes in a few minutes. Our distributed training scales
seamlessly to large corpus sizes and we get comparable and sometimes even up to
45% performance improvement in a variety of NLP benchmarks using models trained
by our distributed procedure which requires of the time taken by the
baseline approach. Finally we also show that we are robust to missing words in
sub-models and are able to effectively reconstruct word representations.Comment: This paper contains 9 pages and has been accepted in the WSDM201
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data
COMET is a single-pass MapReduce algorithm for learning on large-scale data.
It builds multiple random forest ensembles on distributed blocks of data and
merges them into a mega-ensemble. This approach is appropriate when learning
from massive-scale data that is too large to fit on a single machine. To get
the best accuracy, IVoting should be used instead of bagging to generate the
training subset for each decision tree in the random forest. Experiments with
two large datasets (5GB and 50GB compressed) show that COMET compares favorably
(in both accuracy and training time) to learning on a subsample of data using a
serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble
evaluation which dynamically decides how many ensemble members to evaluate per
data point; this can reduce evaluation cost by 100X or more
Squeezing the ensemble pruning: Faster and more accurate categorization for news portals
Recent studies show that ensemble pruning works as effective as traditional ensemble of classifiers (EoC). In this study, we analyze how ensemble pruning can improve text categorization efficiency in time-critical real-life applications such as news portals. The most crucial two phases of text categorization are training classifiers and assigning labels to new documents; but the latter is more important for efficiency of such applications. We conduct experiments on ensemble pruning-based news article categorization to measure its accuracy and time cost. The results show that our heuristics reduce the time cost of the second phase. Also we can make a trade-off between accuracy and time cost to improve both of them with appropriate pruning degrees. © 2012 Springer-Verlag Berlin Heidelberg
Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance
Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for classification purposes and is used with various kinds of uncertain information. Prior to preparing the dataset for classification, the uncertain dataset was first processed through missing data treatment and other uncertainty handling procedures to produce the balanced dataset. Three different real-time datasets, including the Titanic dataset, the PIMA Indian Diabetes dataset, and datasets relating to heart disease, have been used to test the proposed algorithm. The suggested algorithm's performance has been assessed in terms of the precision, recall, f-measure, and accuracy metrics. The outcomes of suggested decision tree and the standard decision tree have been contrasted. On all three datasets, it was found that the decision tree with Gini impurity optimization performed remarkably well
An advance extended binomial GLMBoost ensemble method with synthetic minority over-sampling technique for handling imbalanced datasets
Classification is an important activity in a variety of domains. Class imbalance problem have reduced the performance of the traditional classification approaches. An imbalance problem arises when mismatched class distributions are discovered among the instances of class of classification datasets. An advance extended binomial GLMBoost (EBGLMBoost) coupled with synthetic minority over-sampling technique (SMOTE) technique is the proposed model in the study to manage imbalance issues. The SMOTE is used to solve the proposed model, ensuring that the target variable's distribution is balanced, whereas the GLMBoost ensemble techniques are built to deal with imbalanced datasets. For the entire experiment, twenty different datasets are used, and support vector machine (SVM), Nu-SVM, bagging, and AdaBoost classification algorithms are used to compare with the suggested method. The model's sensitivity, specificity, geometric mean (G-mean), precision, recall, and F-measure resulted in percentages for training and testing datasets are 99.37, 66.95, 80.81, 99.21, 99.37, 99.29 and 98.61, 54.78, 69.88, 98.77, 96.61, 98.68, respectively. With the help of the Wilcoxon test, it is determined that the proposed technique performed well on unbalanced data. Finally, the proposed solutions are capable of efficiently dealing with the problem of class imbalance
A support system for predicting eBay end prices.
In this report a support system for predicting end prices on eBay is
proposed. The end price predictions are based on the item descriptions found in
the item listings of eBay, and on some numerical item features.
The system uses text mining and boosting algorithms from the
field of machine learning.
Our system substantially outperforms the naive method of
predicting the category mean price. Moreover, interpretation of
the model enables us to identify influential terms in the item
descriptions and shows that the item description is more
influential than the seller feedback rating, which was shown to be
influential in earlier studies
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