The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can...Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis （FIA） plots. Methods： Each plot had three forest community type labels： （1） ＂FIA＂ types were assigned by the FIA program using a supervised method; （2） ＂USNVC＂ types were assigned via a key based on the U.S. National Vegetation Classification; （3） ＂empirical＂ types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. Results： The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The Iongleaf pine forest community type stood out as the exception： it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as Ioblolly pine types, indicator species metrics were generally high for the USNVC, suggesting that LJSNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. Conclusions： The classifications have similarities and differences that reflect their differing approaches and Dbjectives. They are most consistent for forest community types that occur in a relatively narrow range of Invironmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, guggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.展开更多
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
基金Funding for this work came from the USDA Forest Service Resources Planning Act Assessment,via an agreement with North Carolina State University
文摘Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis （FIA） plots. Methods： Each plot had three forest community type labels： （1） ＂FIA＂ types were assigned by the FIA program using a supervised method; （2） ＂USNVC＂ types were assigned via a key based on the U.S. National Vegetation Classification; （3） ＂empirical＂ types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. Results： The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The Iongleaf pine forest community type stood out as the exception： it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as Ioblolly pine types, indicator species metrics were generally high for the USNVC, suggesting that LJSNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. Conclusions： The classifications have similarities and differences that reflect their differing approaches and Dbjectives. They are most consistent for forest community types that occur in a relatively narrow range of Invironmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, guggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.
