Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network
Standard classification algorithms are often inaccurate when used in a wireless sensor network (WSN), where the observed data occur in imbalanced classes. The imbalanced data classification problem occurs when the number of samples in one class, usually the class of interest, is much lower than the...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/460641 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547772802269184 |
|---|---|
| author | Hang Yang Simon Fong Raymond Wong Guangmin Sun |
| author_facet | Hang Yang Simon Fong Raymond Wong Guangmin Sun |
| author_sort | Hang Yang |
| collection | DOAJ |
| description | Standard classification algorithms are often inaccurate when used in a wireless sensor network (WSN), where the observed data occur in imbalanced classes. The imbalanced data classification problem occurs when the number of samples in one class, usually the class of interest, is much lower than the number in the other classes. Many classification models have been studied in the data-mining research community. However, they all assume that the input data are stationary and bounded in size, so that resampling techniques and postadjustment by measuring the classification cost can be applied. In this paper, we devise a new scheme that extends a popular stream classification algorithm to the analysis of WSNs for reducing the adverse effects of the imbalanced class in the data. This new scheme is resource light at the algorithm level and does not require any data preprocessing. It uses weighted naïve Bayes predictors at the decision tree leaves to effectively reduce the impact of imbalanced classes. Experiments show that our modified algorithm outperforms the original stream classification algorithm. |
| format | Article |
| id | doaj-art-45628895ff1949e1a33c5caa4cc9d769 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-45628895ff1949e1a33c5caa4cc9d7692025-02-03T06:43:16ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-01-01910.1155/2013/460641Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor NetworkHang Yang0Simon Fong1Raymond Wong2Guangmin Sun3 Department of Computer and Information Science, University of Macau, Taipa, Macau Department of Computer and Information Science, University of Macau, Taipa, Macau School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia Department of Electronic Engineering, Beijing University of Technology, Beijing 100022, ChinaStandard classification algorithms are often inaccurate when used in a wireless sensor network (WSN), where the observed data occur in imbalanced classes. The imbalanced data classification problem occurs when the number of samples in one class, usually the class of interest, is much lower than the number in the other classes. Many classification models have been studied in the data-mining research community. However, they all assume that the input data are stationary and bounded in size, so that resampling techniques and postadjustment by measuring the classification cost can be applied. In this paper, we devise a new scheme that extends a popular stream classification algorithm to the analysis of WSNs for reducing the adverse effects of the imbalanced class in the data. This new scheme is resource light at the algorithm level and does not require any data preprocessing. It uses weighted naïve Bayes predictors at the decision tree leaves to effectively reduce the impact of imbalanced classes. Experiments show that our modified algorithm outperforms the original stream classification algorithm.https://doi.org/10.1155/2013/460641 |
| spellingShingle | Hang Yang Simon Fong Raymond Wong Guangmin Sun Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network International Journal of Distributed Sensor Networks |
| title | Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network |
| title_full | Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network |
| title_fullStr | Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network |
| title_full_unstemmed | Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network |
| title_short | Optimizing Classification Decision Trees by Using Weighted Naïve Bayes Predictors to Reduce the Imbalanced Class Problem in Wireless Sensor Network |
| title_sort | optimizing classification decision trees by using weighted naive bayes predictors to reduce the imbalanced class problem in wireless sensor network |
| url | https://doi.org/10.1155/2013/460641 |
| work_keys_str_mv | AT hangyang optimizingclassificationdecisiontreesbyusingweightednaivebayespredictorstoreducetheimbalancedclassprobleminwirelesssensornetwork AT simonfong optimizingclassificationdecisiontreesbyusingweightednaivebayespredictorstoreducetheimbalancedclassprobleminwirelesssensornetwork AT raymondwong optimizingclassificationdecisiontreesbyusingweightednaivebayespredictorstoreducetheimbalancedclassprobleminwirelesssensornetwork AT guangminsun optimizingclassificationdecisiontreesbyusingweightednaivebayespredictorstoreducetheimbalancedclassprobleminwirelesssensornetwork |