The ID3 algorithm is a classical learning algorithm of decision tree in data mining.The algorithm trends to choosing the attribute with more values,affect the efficiency of classification and prediction for building a...The ID3 algorithm is a classical learning algorithm of decision tree in data mining.The algorithm trends to choosing the attribute with more values,affect the efficiency of classification and prediction for building a decision tree.This article proposes a new approach based on an improved ID3 algorithm.The new algorithm introduces the importance factor λ when calculating the information entropy.It can strengthen the label of important attributes of a tree and reduce the label of non-important attributes.The algorithm overcomes the flaw of the traditional ID3 algorithm which tends to choose the attributes with more values,and also improves the efficiency and flexibility in the process of generating decision trees.展开更多
A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nuc...A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nucleus. Connected pixels on the circle were applied to compare the intensity value with the nucleus, with the membership function used to give the fuzzy result. The pixel with maximum information gain was chosen as the parent node to build a binary decision tree. Thus, the comer detector was derived. The pictures taken in Fengtai Railway Station in Beijing were used to test the method. The experimental results show that when the number of pixels on the test mask is chosen to be 9, best result can be obtained. The comer detector significantly outperforms existing detector in computational efficiency without sacrificing the quality and the method also provides high performance against Poisson noise and Gaussian blur.展开更多
In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (re...In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.展开更多
Three common species of Miniopterus fuliginosus,M.magnater and M.pusillus are known to inhabit China.However,M.fuliginosus and M.magnater are so similar in external morphology as to pose great challenges for accurate ...Three common species of Miniopterus fuliginosus,M.magnater and M.pusillus are known to inhabit China.However,M.fuliginosus and M.magnater are so similar in external morphology as to pose great challenges for accurate classification.Furthermore,taxonomic statuses,distribution ranges and taxonomic keys of these three species have remained controversial.For addressing these outstanding issues,the authors integrated molecular phylogenetic analyses,ensemble species distribution models(ESDMs),multiple morphological comparisons and decision tree algorithms for reassessing their taxonomy and distribution in China.Mitochondrial cytochrome c oxidase subunit I(COI)gene phylogeny revealed three distinct monophyletic groups corresponding to M.fuliginosus,M.magnater and M.pusillus.And the observed distribution patterns indicated M.fuliginosus had a broad distribution across China while M.magnater and M.pusillus exhibited a more restricted distribution,overlapping with M.fuliginosus in South China.And cranial morphometry indicated M.magnater was slightly larger than M.fuliginosus and significantly larger than M.pusillus.Also three-dimensional(3D)skull geomorphometry uncovered distinct features for each species in rostrum,braincase,tympanic bullae and mandibular shape.Decision tree algorithms helped to identify forearm length,braincase breadth and width across the third upper molars as three major taxonomic keys for assisting species identification.This study corroborated the importance of integrative approaches for identifying Miniopterus species and validated a methodological approach applicable to other cryptic species complexes.展开更多
文摘The ID3 algorithm is a classical learning algorithm of decision tree in data mining.The algorithm trends to choosing the attribute with more values,affect the efficiency of classification and prediction for building a decision tree.This article proposes a new approach based on an improved ID3 algorithm.The new algorithm introduces the importance factor λ when calculating the information entropy.It can strengthen the label of important attributes of a tree and reduce the label of non-important attributes.The algorithm overcomes the flaw of the traditional ID3 algorithm which tends to choose the attributes with more values,and also improves the efficiency and flexibility in the process of generating decision trees.
基金Project(J2008X011) supported by the Natural Science Foundation of Ministry of Railway and Tsinghua University,China
文摘A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nucleus. Connected pixels on the circle were applied to compare the intensity value with the nucleus, with the membership function used to give the fuzzy result. The pixel with maximum information gain was chosen as the parent node to build a binary decision tree. Thus, the comer detector was derived. The pictures taken in Fengtai Railway Station in Beijing were used to test the method. The experimental results show that when the number of pixels on the test mask is chosen to be 9, best result can be obtained. The comer detector significantly outperforms existing detector in computational efficiency without sacrificing the quality and the method also provides high performance against Poisson noise and Gaussian blur.
文摘In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.
基金the National Natural Sciences Foundation of China(32192421)the Special Grant Foundations for National Science and &Technology Basic Research Program of China(2021FY100303)the DFGP Project of Fauna of Guangdong Province(202115)。
文摘Three common species of Miniopterus fuliginosus,M.magnater and M.pusillus are known to inhabit China.However,M.fuliginosus and M.magnater are so similar in external morphology as to pose great challenges for accurate classification.Furthermore,taxonomic statuses,distribution ranges and taxonomic keys of these three species have remained controversial.For addressing these outstanding issues,the authors integrated molecular phylogenetic analyses,ensemble species distribution models(ESDMs),multiple morphological comparisons and decision tree algorithms for reassessing their taxonomy and distribution in China.Mitochondrial cytochrome c oxidase subunit I(COI)gene phylogeny revealed three distinct monophyletic groups corresponding to M.fuliginosus,M.magnater and M.pusillus.And the observed distribution patterns indicated M.fuliginosus had a broad distribution across China while M.magnater and M.pusillus exhibited a more restricted distribution,overlapping with M.fuliginosus in South China.And cranial morphometry indicated M.magnater was slightly larger than M.fuliginosus and significantly larger than M.pusillus.Also three-dimensional(3D)skull geomorphometry uncovered distinct features for each species in rostrum,braincase,tympanic bullae and mandibular shape.Decision tree algorithms helped to identify forearm length,braincase breadth and width across the third upper molars as three major taxonomic keys for assisting species identification.This study corroborated the importance of integrative approaches for identifying Miniopterus species and validated a methodological approach applicable to other cryptic species complexes.
