Induction of decision trees

Cited by (1,063)

Items 401–500 of 1,063

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2. Symbolic and Neural Learning Algorithms: An Experimental Comparison
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3. Comparative analysis of data mining methods for bankruptcy prediction
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4. Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online
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5. Generalizing DTW to the multi-dimensional case requires an adaptive approach
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6. Recent advances and emerging challenges of feature selection in the context of big data
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7. Artificial intelligence and machine learning in spine research
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8. Classification Techniques in Machine Learning: Applications and Issues
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10. Discovery of frequent DATALOG patterns
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11. On the application of machine learning techniques to derive seismic fragility curves
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12. Fast and Accurate Time Series Classification with WEASEL
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13. Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe
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15. Machine Learning in Materials Science
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16. Overfitting Avoidance as Bias
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17. The Utility of Knowledge in Inductive Learning
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18. Unbiased split selection for classification trees based on the Gini Index
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19. Deep learning for biological image classification
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20. Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data
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21. Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis
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22. Rice plant disease classification using color features: a machine learning paradigm
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23. Detecting code smells using machine learning techniques: Are we there yet?
    Dario Di Nucci, Fabio Palomba, Damian A. Tamburri, Alexander Serebrenik, Andrea De Lucia · 2018 · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
24. Maximal consistent block technique for rule acquisition in incomplete information systems
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25. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information
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26. Feature selection for high-dimensional data
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27. INDUCTIVE AND BAYESIAN LEARNING IN MEDICAL DIAGNOSIS
    Igor Kononenko · 1993 · Applied Artificial Intelligence
28. Ensemble feature selection: Homogeneous and heterogeneous approaches
    Borja Seijo-Pardo, Iago Porto-Díaz, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos · 2016 · Knowledge-Based Systems
29. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project
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30. Approaches to the Automatic Discovery of Patterns in Biosequences
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31. UNKNOWN ATTRIBUTE VALUES IN INDUCTION
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32. Neural network models and the prediction of bank bankruptcy
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33. Decision tree-based diagnosis of coronary artery disease: CART model
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34. Collaborative Filtering with the Simple Bayesian Classifier
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35. Machine learning-driven new material discovery
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36. MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud1
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37. Leucocyte classification for leukaemia detection using image processing techniques
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38. Machine Learning for Health Services Researchers
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39. Trading spaces: Computation, representation, and the limits of uninformed learning
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40. A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes
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41. BOAT—optimistic decision tree construction
    Johannes E. Gehrke, Venkatesh Ganti, Raghu Ramakrishnan, Wei‐Yin Loh · 1999 · ACM SIGMOD Record
42. Digital image analysis in breast pathology—from image processing techniques to artificial intelligence
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43. TAILOR: a record linkage toolbox
    Mohamed G. Elfeky, Vassilios S. Verykios, Ahmed K. Elmagarmid · 2003 · IEEE International Conference on Data Engineering (ICDE)
44. Machine learning in autistic spectrum disorder behavioral research: A review and ways forward
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45. Towards explainable deep neural networks (xDNN)
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46. Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir
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47. Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
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48. A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems
    Daniele Micci Barreca · 2001 · ACM SIGKDD Explorations Newsletter
49. DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling
    Haipeng Cai, Na Meng, Barbara G. Ryder, Daphne Yao · 2018 · IEEE Transactions on Information Forensics and Security
50. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage
    Sarker, Iqbal H., Kayes, A. S. M., Watters, Paul · 2019 · Journal of Big Data
51. A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification
    R. Saravanan, Pothula Sujatha · 2018 · International Conference on Intelligent Computing and Control Systems (ICICCS)
52. Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms
    Kshyanaprava Panda Panigrahi, Himansu Das, Abhaya Kumar Sahoo, Suresh Chandra Moharana · 2020 · Advances in intelligent systems and computing
53. Extreme re-balancing for SVMs
    Bhavani Raskutti, Adam Kowalczyk · 2004 · ACM SIGKDD Explorations Newsletter
54. Comparison of three data mining models for predicting diabetes or prediabetes by risk factors
    Xue‐Hui Meng, Yixiang Huang, Dongping Rao, Qiu Zhang, Qing Liu · 2012 · The Kaohsiung Journal of Medical Sciences
55. JAM: java agents for meta-learning over distributed databases
    Salvatore J. Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee B, Dave W. Fan, Philip K. Chan · 1997
56. openModeller: a generic approach to species’ potential distribution modelling
    Mauro Enrique de Souza Muñoz, Renato De Giovanni, Marinez Ferreira de Siqueira, Tim Sutton, Peter G. Brewer, Ricardo Scachetti Pereira, Dora Ann Lange Canhos, Vanderlei Perez Canhos · 2009 · GeoInformatica
57. Predicting ADME properties in silico: methods and models
    Darko Butina, Matthew Segall, Katrina E. Frankcombe · 2002 · Drug Discovery Today
58. Entropy nets: from decision trees to neural networks
    Ishwar K. Sethi · 1990 · Proceedings of the IEEE
59. Predictive Statistical Models for User Modeling
    Ingrid Zukerman A, David W. Albrecht · 2001 · User Modeling and User-Adapted Interaction
60. A new machine learning model based on induction of rules for autism detection
    Fadi Thabtah, David Peebles · 2019 · Health Informatics Journal
61. Hyperspectral data analysis and supervised feature reduction via projection pursuit
    L.O. Jimenez, D. A. Landgrebe · 1999 · IEEE Transactions on Geoscience and Remote Sensing
62. A comprehensive review of current local features for computer vision
    Jing Li, Nigel M Allinson · 2008 · Neurocomputing
63. On biases in estimating multi-valued attributes
    Igor Kononenko · 1995
64. Cascade Generalization
    João Manuel Portela da Gama, Pavel Brazdil · 2000 · Machine Learning
65. A systematic literature review for network intrusion detection system (IDS)
    Abdulganiyu, Oluwadamilare Harazeem, Ait Tchakoucht, Taha, Saheed, Yakub Kayode · 2023 · International Journal of Information Security
66. On the Interaction of Theory and Data in Concept Learning
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67. Concept-Based Information Retrieval Using Explicit Semantic Analysis
    Ofer Egozi, Shaul Markovitch, Evgeniy Gabrilovich · 2011 · ACM Transactions on Information Systems
68. Prediction of cement-based mortars compressive strength using machine learning techniques
    Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, Evgenios A. Kotsonis, Paulo Barbosa Lourenço · 2021 · Neural Computing and Applications
69. Learning from examples: generation and evaluation of decision trees for software resource analysis
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70. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation
    Dirk Woetzel, René Huber, Peter Kupfer, Dirk Pohlers, Michael Pfaff, Dominik Driesch, Thomas Häupl, Dirk Koczan, Peter Stiehl, Reinhard Guthke et al. · 2014 · Arthritis Research & Therapy
71. The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices
    Hristijan Gjoreski, Mathias Ciliberto, Lin Wang, Francisco Javier Ordóñez Morales, Sami Mekki, Stefan Valentin, Daniel Roggen · 2018 · IEEE Access
72. Artificial intelligence in breast imaging
    Elizabeth Le, Yanzhong Wang, Yuan Huang, Sarah E. Hickman, Fiona J. Gilbert · 2019 · Clinical Radiology
73. A Survey of Methods for Scaling Up Inductive Algorithms
    Foster John Provost, Venkateswarlu Kolluri · 1999 · Data Mining and Knowledge Discovery
74. Landslide susceptibility mapping in Injae, Korea, using a decision tree
    Young-Kwang Yeon, Jong-Gyu Han, Keun Ho Ryu · 2010 · Engineering Geology
75. Support Vector Learning for Semantic Argument Classification
    Sameer Pradhan, Kadri Hacıoğlu, Valerie Krugler, Wayne Ward, James Martin, Daniel S. Jurafsky · 2005 · Machine Learning
76. The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process
    Matthias Elter, R Schulz-Wendtland, Thomas C. Wittenberg · 2007 · Medical Physics
77. Facial Attractiveness: Beauty and the Machine
    Yael Eisenthal, Gideon Dror, Eytan Ruppin · 2005 · Neural Computation
78. Turning from TF-IDF to TF-IGM for term weighting in text classification
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79. Advances in machine learning and IoT for water quality monitoring: A comprehensive review
    Ismail Essamlali, Hasna Nhaila, Mohamed El Khaïli · 2024 · Heliyon
80. Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms
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81. Decision tree and PCA-based fault diagnosis of rotating machinery
    Sun, Weixiang, Chen, Jin, Li, Jiaqing · 2007 · Mechanical Systems and Signal Processing
82. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods
    Gretchen G. Moisen, Elizabeth A. Freeman, Jock A. Blackard, Tracey S. Frescino, Niklaus E. Zimmermann, Thomas C. Edwards · 2006 · Ecological Modelling
83. Simplifying decision trees
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84. A review on the combination of binary classifiers in multiclass problems
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85. A conceptual version of the K-means algorithm
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86. A Further Comparison of Splitting Rules for Decision-Tree Induction
    Wray Buntine, Tim Niblett · 1992 · Machine Learning
87. Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling
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88. Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning
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89. Data mining using two-dimensional optimized association rules
    Takeshi Fukuda, Yasukiko Morimoto, Shinichi Morishita, Takeshi Tokuyama · 1996 · ACM SIGMOD International Conference on Management of Data (SIGMOD)
90. Development of machine learning models for diagnosis of glaucoma
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91. Knowledge Discovery: Methods from data mining and machine learning
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92. A survey of the applications of text mining in financial domain
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93. Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree
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94. Information-theoretic measures of uncertainty for rough sets and rough relational databases
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95. Analysis of traffic accident severity using Decision Rules via Decision Trees
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96. A feature selection algorithm of decision tree based on feature weight
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97. An Interval Classifier for Database Mining Applications
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98. Empirically guided software development using metric-based classification trees
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99. Set-oriented mining for association rules in relational databases
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100. World-scale mining of objects and events from community photo collections
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