RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data

• URL: http://arxiv.org/abs/2208.06436v1
• Date: Thu, 11 Aug 2022 13:55:04 GMT
• Title: RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
• Authors: Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Elina Francovic-Fontaine, Alexandre Drouin, Jacques Corbeil
• Abstract summary: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
• Score: 59.4141628321618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.

Related papers

• Machine Learning Based Missing Values Imputation in Categorical Datasets [2.5611256859404983]
  This research looked into the use of machine learning algorithms to fill in the gaps in categorical datasets. The emphasis was on ensemble models constructed using the Error Correction Output Codes framework. Deep learning for missing data imputation has obstacles despite these encouraging results, including the requirement for large amounts of labeled data.
  arXiv  Detail & Related papers  (2023-06-10T03:29:48Z)
• Topological Quality of Subsets via Persistence Matching Diagrams [0.196629787330046]
  We measure the quality of a subset concerning the dataset it represents using topological data analysis techniques. In particular, this approach enables us to explain why the chosen subset is likely to result in poor performance of a supervised learning model.
  arXiv  Detail & Related papers  (2023-06-04T17:08:41Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models [16.436293069942312]
  We are interested in learning probabilistic generative models from high-dimensional heterogeneous data in an unsupervised fashion. We propose a general framework that combines disparate data types through the exponential family of distributions. The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.
  arXiv  Detail & Related papers  (2021-08-27T18:10:31Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Harmonization with Flow-based Causal Inference [12.739380441313022]
  This paper presents a normalizing-flow-based method to perform counterfactual inference upon a structural causal model (SCM) to harmonize medical data. We evaluate on multiple, large, real-world medical datasets to observe that this method leads to better cross-domain generalization compared to state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-06-12T19:57:35Z)
• Model-agnostic interpretation by visualization of feature perturbations [0.0]
  We propose a model-agnostic interpretation approach that uses visualization of feature perturbations induced by the particle swarm optimization algorithm. We validate our approach both qualitatively and quantitatively on publicly available datasets.
  arXiv  Detail & Related papers  (2021-01-26T00:53:29Z)
• Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays [62.997667081978825]
  Generalized Linear Latent Variable models (GLLVMs) generalize such factor models to non-Gaussian responses. Current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets. We propose a new approach for fitting GLLVMs to high-dimensional datasets, based on approximating the model using penalized quasi-likelihood.
  arXiv  Detail & Related papers  (2020-10-06T04:28:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.