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Handling Missing Data in Decision Trees: A Probabilistic Approach [article]

Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck
2020 arXiv   pre-print
Concurrently, missing data is a prevalent occurrence that hinders performance of machine learning models. As such, handling missing data in decision trees is a well studied problem.  ...  Decision trees are a popular family of models due to their attractive properties such as interpretability and ability to handle heterogeneous data.  ...  The authors would like to thank Steven Holtzen for initial discussions about expected prediction for decision trees.  ... 
arXiv:2006.16341v1 fatcat:isdt37t2nfgqdelu2h2jhz4g2m

A compositional approach to probabilistic knowledge compilation

Giso H. Dal, Alfons W. Laarman, Arjen Hommersom, Peter J.F. Lucas
2021 International Journal of Approximate Reasoning  
However, these methods require a computationally intensive compilation step in search of these patterns, which effectively prohibits the handling of larger BNs.  ...  In this paper, we propose a solution to this problem by extending WMC methods with a framework called Compositional Weighted Model Counting (CWMC).  ...  Data locality plays a major role in performance. Quite simply, there are more cache misses for monolithic representations than there are for composed representations.  ... 
doi:10.1016/j.ijar.2021.07.007 fatcat:mnwvssjucrb2zpc5t33gnfft7e

A Simple Domain-Independent Probabilistic Approach to Generation

Gabor Angeli, Percy Liang, Dan Klein
2010 Conference on Empirical Methods in Natural Language Processing  
In our approach, we break up the end-to-end generation process into a sequence of local decisions, arranged hierarchically and each trained discriminatively.  ...  We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework.  ...  our approach, operates in a log-linear framework.  ... 
dblp:conf/emnlp/AngeliLK10 fatcat:d7cabsu65nc53lrs5rtfchwcbq

Pattern Inference Theory: A Probabilistic Approach to Vision [chapter]

Daniel Kersten, Paul Schrater
2005 Perception and the Physical World  
Key missing components include the ability to model uncertainty, probabilistic modeling of pattern synthesis as a necessary prerequisite to understanding pattern inference, the means to handle the complexity  ...  Bayes provides a minimal formalism that can deal with the sophistication and versatility of perception missing from some other approaches.  ...  However, these approaches are limited in requiring the availability of sample pairs of scene and image data.  ... 
doi:10.1002/0470013427.ch7 fatcat:l2itz6jy6vhhnfb4ybg3m5uhry

EntropyDB: A Probabilistic Approach to Approximate Query Processing [article]

Laurel Orr, Magdalena Balazinska, Dan Suciu
2019 arXiv   pre-print
We present EntropyDB, an interactive data exploration system that uses a probabilistic approach to generate a small, query-able summary of a dataset.  ...  Departing from traditional summarization techniques, we use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate query answers.  ...  Sketches have the benefit of being able to handle streaming data but are usually built to handle a limited number of queries.  ... 
arXiv:1911.04948v1 fatcat:gi5byc24w5elndo7lanhovtba4

Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach

RICCARDO ZESE, GIUSEPPE COTA, EVELINA LAMMA, ELENA BELLODI, FABRIZIO RIGUZZI
2019 Theory and Practice of Logic Programming  
To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics (DLs) with probability theory.  ...  The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD).  ...  Example 8 Consider, for example, the rule ∃ defined as ({(x : ∃S.C), miss(z, {(x, z) : S, z : C})}, {}) → {{anon(y), ((x, y) : S), (y : C)}} handling existential restrictions.  ... 
doi:10.1017/s1471068418000480 fatcat:dp2liz4ygrggdbopkuwpxuhxw4

A probabilistic approach to socio-geographic reality mining

Katayoun Farrahi
2011 ACM SIGMultimedia Records  
Acknowledgements There are many people to which I am indebted during this last step in my seemingly life-long journey as a student! Foremost, I would like to thank my advisor Daniel.  ...  Big thank you to Iacopo and Corinne who helped us in many ways and showed us some special places in Switzerland.  ...  The TM approach predicts missing location data as well as the PD approach, however, our approach outperforms the PD method for predicting missing proximity data.  ... 
doi:10.1145/2069203.2069206 fatcat:b4veqpnsajdrbhm4xvugd3qop4

A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources [article]

Jie Wang, Luyan Ji, Xiaomeng Huang, Haohuan Fu, Shiming Xu, Congcong Li
2016 arXiv   pre-print
A probabilistic graphical model (PGM) approach is proposed in this research for 30 m resolution land-cover mapping with multi-temporal Landsat and MODerate Resolution Imaging Spectroradiometer (MODIS)  ...  Independent classifiers were applied to two single-date Landsat 8 scenes and the MODIS time-series data, respectively, for probability estimation. A PGM was created for each pixel in Landsat 8 data.  ...  RF is an ensemble classifier that consists of multiple decision trees. It can maintain accuracy when missing values exist (Rodriguez-Galiano, Ghimire, et al. 2012; ).  ... 
arXiv:1612.03373v1 fatcat:gvuclatfrrcpdnwppzhvl5ubiq

A consistent approach for probabilistic residential flood loss modeling in Europe

Stefan Lüdtke, Kai Schröter, Max Steinhausen, Laura Weise, Rui Figueiredo, Heidi Kreibich
2019 Water Resources Research  
Plain Language Summary We present a novel, probabilistic approach to calculate flood losses for residential buildings in Europe.  ...  To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the  ...  In addition, this work was supported by Climate-KIC through project "SAFERPLACES -Improved assessment of pluvial, fluvial and coastal flood hazards andrisks in European cities as a mean to build safer  ... 
doi:10.1029/2019wr026213 fatcat:gfpn4z7zajawjm5zafmg2l5lqm

Probabilistic Forecasting of Surface Ozone with a Novel Statistical Approach

Nikolay V. Balashov, Anne M. Thompson, George S. Young
2017 Journal of Applied Meteorology and Climatology  
In this way, when a regression is developed for a specific regime, data from the other regimes are also used, with weights based on their similarity to this specific regime.  ...  This approach, regression in SOM (REGiS), yields a distinct model for each regime taking into account both the training cases for that regime and other similar training cases.  ...  Using various meteorological variables, the CART method generates a decision tree with a number of categories, which represent various modes of ozone pollutionsome are polluted, some are moderate, and  ... 
doi:10.1175/jamc-d-16-0110.1 fatcat:2gjp6zc33nabzfgyhx55gr4w3m

A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU

Peter J.F. Lucas, Nicolette C. de Bruijn, Karin Schurink, Andy Hoepelman
2000 Artificial Intelligence in Medicine  
In this paper, we describe the development of a probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the intensive-care unit.  ...  The patient data that were available were of limited value in the initial construction of the models because of problems of incompleteness. In particular, detailed temporal information was missing.  ...  Moreover, these systems offer a natural environment for facilities of decision support that assist clinicians in handling hard clinical problems. The reasons for this are twofold: 1.  ... 
doi:10.1016/s0933-3657(00)00048-8 pmid:10906615 fatcat:nqicdb5su5b5jce26nhy7scg3u

β Algorithm: A New Probabilistic Process Learning Approach For Big Data In Healthcare

Maha Zayoud, Yehia Kotb, Sorin Ionescu.
2019 IEEE Access  
In this paper, a new process learning framework that is based on probabilistic learning and predicate logic is proposed.  ...  This paper targets a methodology of learning processes given data and the learning algorithm finds out the logical operators that bind the events described in data and model it using predicate logic.  ...  Though this approach provides great control to the user, the user needs to control the mechanism for handling the data flow.  ... 
doi:10.1109/access.2019.2922635 fatcat:pngqg2srxzhjjgljpyfwtdghfm

Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach [chapter]

Michelangelo Ceci, Annalisa Appice, Donato Malerba
2004 Lecture Notes in Computer Science  
In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account.  ...  The method is implemented in a Data Mining system tightly integrated with an object relational spatial database.  ...  through Manchester Computing in the context of the IST European project SPIN!  ... 
doi:10.1007/978-3-540-30116-5_12 fatcat:yy2eo6ux4ndwrpzzlxwriavfiy

A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization [article]

Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin
2018 arXiv   pre-print
We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories in narrative ultrasound (US) reports.  ...  No human-labelled data was required in any step of this study; for training, LI-RADS scores were automatically extracted from those reports that contained structured LI-RADS scores, and it translated the  ...  ACKNOWLEDGEMENTS This work was supported in part by grants from the National Cancer Institute, National Institutes of Health, 1U01CA190214 and 1U01CA187947.  ... 
arXiv:1806.07346v1 fatcat:xffc5aibhjhljnscqftga5ye3u

A probabilistic approach to inference with limited information in sensor networks

Rahul Biswas, Sebastian Thrun, Leonidas J. Guibas
2004 Proceedings of the third international symposium on Information processing in sensor networks - IPSN'04  
We present a methodology for a sensor network to answer queries with limited and stochastic information using probabilistic techniques.  ...  This capability is useful in that it allows sensor networks to answer queries effectively even when present information is partially corrupt and when more information is unavailable or too costly to obtain  ...  in the scenario presented and is able to handle a wide range of types of sensor information.  ... 
doi:10.1145/984622.984662 dblp:conf/ipsn/BiswasTG04 fatcat:kqtal7su5ncchha34z3ombcuvu
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