The Experts below are selected from a list of 10752 Experts worldwide ranked by ideXlab platform
Ahmad Taherkhani - One of the best experts on this subject based on the ideXlab platform.
-
recognizing sorting algorithms with the c4 5 Decision Tree Classifier
International Conference on Program Comprehension, 2010Co-Authors: Ahmad TaherkhaniAbstract:We present a method for automatic algorithm recognition, which consists of two phases. First, the target algorithms are converted into characteristic vectors, which are computed based on static analysis of program code including various statistics of language constructs and analysis of Roles of Variables. In the second phase, the algorithms are classified based on these vectors using the C4.5 Decision Tree Classifier. We have developed a prototype and successfully applied the method to sorting algorithms. Evaluated with leave-one-out technique, the accuracy of the constructed Decision Tree Classifier is 97.1%.
-
ICPC - Recognizing Sorting Algorithms with the C4.5 Decision Tree Classifier
2010 IEEE 18th International Conference on Program Comprehension, 2010Co-Authors: Ahmad TaherkhaniAbstract:We present a method for automatic algorithm recognition, which consists of two phases. First, the target algorithms are converted into characteristic vectors, which are computed based on static analysis of program code including various statistics of language constructs and analysis of Roles of Variables. In the second phase, the algorithms are classified based on these vectors using the C4.5 Decision Tree Classifier. We have developed a prototype and successfully applied the method to sorting algorithms. Evaluated with leave-one-out technique, the accuracy of the constructed Decision Tree Classifier is 97.1%.
Bruce Margon - One of the best experts on this subject based on the ideXlab platform.
-
a census of object types and redshift estimates in the sdss photometric catalog from a trained Decision Tree Classifier
The Astronomical Journal, 2005Co-Authors: A A Suchkov, R J Hanisch, Bruce MargonAbstract:We have applied ClassX, an oblique Decision Tree Classifier optimized for astronomical analysis, to the homogeneous multicolor imaging database of the Sloan Digital Sky Survey (SDSS), training the software on subsets of SDSS objects whose nature is precisely known via spectroscopy. We find that the software, using photometric data only, correctly classifies a very large fraction of the objects with existing SDSS spectra, both stellar and extragalactic. ClassX also accurately predicts the redshifts of both normal and active galaxies in SDSS. To illustrate ClassX applications in SDSS research, we (1) derive the object content of the SDSS Data Release 2 photometric catalog and (2) provide a sample catalog of resolved SDSS objects that contains a large number of candidate active galactic nucleus (AGN) galaxies (27,000), along with 63,000 candidate normal galaxies at magnitudes substantially fainter than the typical magnitudes of SDSS spectroscopic objects. The surface density of AGNs selected by ClassX to i ~ 19 is in agreement with that quoted by SDSS. When ClassX is applied to photometric data fainter than the SDSS spectroscopic limit, the inferred surface density of AGNs rises sharply, as expected. The ability of the Classifier to accurately constrain the redshifts of huge numbers (ultimately ~107) of active galaxies in the photometric database promises new insights into fundamental issues of AGN research, such as the evolution of the AGN luminosity function with cosmic time, the starburst-AGN connection, and AGN–galactic morphology relationships.
-
a census of object types and redshift estimates in the sdss photometric catalog from a trained Decision Tree Classifier
arXiv: Astrophysics, 2005Co-Authors: A A Suchkov, R J Hanisch, Bruce MargonAbstract:We have applied ClassX, an oblique Decision Tree Classifier optimized for astronomical analysis, to the homogeneous multicolor imaging data base of the Sloan Digital Sky Survey (SDSS), training the software on subsets of SDSS objects whose nature is precisely known via spectroscopy. We find that the software, using photometric data only, correctly classifies a very large fraction of the objects with existing SDSS spectra, both stellar and extragalactic. ClassX also accurately predicts the redshifts of both normal and active galaxies in SDSS. To illustrate ClassX applications in SDSS research, we (a) derive the object content of the SDSS DR2 photometric catalog and (b) provide a sample catalog of resolved SDSS objects that contains a large number of candidate AGN galaxies, 27,000, along with 63,000 candidate normal galaxies at magnitudes substantially fainter than typical magnitudes of SDSS spectroscopic objects. The surface density of AGN selected by ClassX to i~19 is in agreement with that quoted by SDSS. When ClassX is applied to the photometric data fainter than the SDSS spectroscopic limit, the inferred surface density of AGN rises sharply, as expected. The ability of the Classifier to accurately constrain the redshifts of huge numbers (ultimately ~ 10^7) of active galaxies in the photometric data base promises new insights into fundamental issues of AGN research, such as the evolution of the AGN luminosity function with cosmic time, the starburst--AGN connection, and AGN--galactic morphology relationships.
D A Landgrebe - One of the best experts on this subject based on the ideXlab platform.
-
A Decision Tree Classifier design for high-dimensional data with limited training samples
IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium, 1996Co-Authors: S. Tadjudin, D A LandgrebeAbstract:Advances in sensor technology have increased the spectral resolution of remote sensing data significantly. Higher spectral resolution for each pixel should make possible the discrimination of a larger number of classes in more detail. However, due to the scarcity of training samples in remote sensing applications, the increase in spectral dimensionality only complicates the design of Classifiers which, if not properly done, may cause the deterioration of classification accuracy. In this work, we propose a new design procedure for a hybrid Decision Tree Classifier which improves the classification efficiency and accuracy for classifying high-dimensional data with a small training sample size. We further propose to use a feature extraction technique based on maximizing the statistical distance between two subgroups. Experimental results show that the proposed Tree Classifier is more effective in classifying high-dimensional data with limited training samples than a single-layer Classifier and a previously proposed hybrid Tree Classifier.
-
a survey of Decision Tree Classifier methodology
Systems Man and Cybernetics, 1991Co-Authors: S R Safavian, D A LandgrebeAbstract:A survey is presented of current methods for Decision Tree Classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state Classifiers, the subjects of Tree structure design, feature selection at each internal node, and Decision and search strategies are discussed. The relation between Decision Trees and neutral networks (NN) is also discussed. >
Joe Carthy - One of the best experts on this subject based on the ideXlab platform.
-
Textual Entailment Recognition Using a Linguistically-Motivated Decision Tree Classifier
Lecture Notes in Computer Science, 2006Co-Authors: Eamonn Newman, Nicola Stokes, John Dunnion, Joe CarthyAbstract:In this paper we present a Classifier for Recognising Textual Entailment (RTE) and Semantic Equivalence. We evaluate the performance of this Classifier using an evaluation framework provided by the PASCAL RTE Challenge Workshop. Sentence-pairs are represented as a set of features, which are used by our Decision Tree Classifier to determine if an entailment relationship exisits between each sentence-pair in the RTE test corpus.
-
MLCW - Textual entailment recognition using a linguistically–motivated Decision Tree Classifier
Machine Learning Challenges. Evaluating Predictive Uncertainty Visual Object Classification and Recognising Tectual Entailment, 2006Co-Authors: Eamonn Newman, Nicola Stokes, John Dunnion, Joe CarthyAbstract:In this paper we present a Classifier for Recognising Textual Entailment (RTE) and Semantic Equivalence. We evaluate the performance of this Classifier using an evaluation framework provided by the PASCAL RTE Challenge Workshop. Sentence–pairs are represented as a set of features, which are used by our Decision Tree Classifier to determine if an entailment relationship exisits between each sentence–pair in the RTE test corpus.
A A Suchkov - One of the best experts on this subject based on the ideXlab platform.
-
a census of object types and redshift estimates in the sdss photometric catalog from a trained Decision Tree Classifier
The Astronomical Journal, 2005Co-Authors: A A Suchkov, R J Hanisch, Bruce MargonAbstract:We have applied ClassX, an oblique Decision Tree Classifier optimized for astronomical analysis, to the homogeneous multicolor imaging database of the Sloan Digital Sky Survey (SDSS), training the software on subsets of SDSS objects whose nature is precisely known via spectroscopy. We find that the software, using photometric data only, correctly classifies a very large fraction of the objects with existing SDSS spectra, both stellar and extragalactic. ClassX also accurately predicts the redshifts of both normal and active galaxies in SDSS. To illustrate ClassX applications in SDSS research, we (1) derive the object content of the SDSS Data Release 2 photometric catalog and (2) provide a sample catalog of resolved SDSS objects that contains a large number of candidate active galactic nucleus (AGN) galaxies (27,000), along with 63,000 candidate normal galaxies at magnitudes substantially fainter than the typical magnitudes of SDSS spectroscopic objects. The surface density of AGNs selected by ClassX to i ~ 19 is in agreement with that quoted by SDSS. When ClassX is applied to photometric data fainter than the SDSS spectroscopic limit, the inferred surface density of AGNs rises sharply, as expected. The ability of the Classifier to accurately constrain the redshifts of huge numbers (ultimately ~107) of active galaxies in the photometric database promises new insights into fundamental issues of AGN research, such as the evolution of the AGN luminosity function with cosmic time, the starburst-AGN connection, and AGN–galactic morphology relationships.
-
a census of object types and redshift estimates in the sdss photometric catalog from a trained Decision Tree Classifier
arXiv: Astrophysics, 2005Co-Authors: A A Suchkov, R J Hanisch, Bruce MargonAbstract:We have applied ClassX, an oblique Decision Tree Classifier optimized for astronomical analysis, to the homogeneous multicolor imaging data base of the Sloan Digital Sky Survey (SDSS), training the software on subsets of SDSS objects whose nature is precisely known via spectroscopy. We find that the software, using photometric data only, correctly classifies a very large fraction of the objects with existing SDSS spectra, both stellar and extragalactic. ClassX also accurately predicts the redshifts of both normal and active galaxies in SDSS. To illustrate ClassX applications in SDSS research, we (a) derive the object content of the SDSS DR2 photometric catalog and (b) provide a sample catalog of resolved SDSS objects that contains a large number of candidate AGN galaxies, 27,000, along with 63,000 candidate normal galaxies at magnitudes substantially fainter than typical magnitudes of SDSS spectroscopic objects. The surface density of AGN selected by ClassX to i~19 is in agreement with that quoted by SDSS. When ClassX is applied to the photometric data fainter than the SDSS spectroscopic limit, the inferred surface density of AGN rises sharply, as expected. The ability of the Classifier to accurately constrain the redshifts of huge numbers (ultimately ~ 10^7) of active galaxies in the photometric data base promises new insights into fundamental issues of AGN research, such as the evolution of the AGN luminosity function with cosmic time, the starburst--AGN connection, and AGN--galactic morphology relationships.
