Fast Markov Blanket Discovery Without Causal Sufficiency

2020 ◽  
Author(s):  
Pedro V. B. Jeronymo ◽  
Carlos D. Maciel
Keyword(s):  
Causal Structure ◽  
Learning Task ◽  
High Dimensional ◽  
Target Variable ◽  
Common Assumption ◽  
Markov Blanket ◽  
Common Causes ◽  
Real World Datasets ◽  
Selection Algorithms ◽  
Discovery Algorithms

Faster feature selection algorithms become a necessity as Big Data dictates the zeitgeist. An important class of feature selectors are Markov Blanket (MB) learning algorithms. They are Causal Discovery algorithms that learn the local causal structure of a target variable. A common assumption in their theoretical basis, yet often violated in practice, is causal sufficiency: the requirement that all common causes of the measured variables in the dataset are also in the dataset. Recently, Yu et al. (2018) proposed the M3B algorithm, the first to directly learn the MB without demanding causal sufficiency. The main drawback of M3B is that it is time inefficient, being intractable for high-dimensional inputs. In this paper, we derive the Fast Markov Blanket Discovery Algorithm (FMMB). Empirical results that compare FMMB to M3B on the structural learning task show that FMMB outperforms M3B in terms of time efficiency while preserving structural accuracy. Five real-world datasets where used to contrast both algorithms as feature selectors. Applying NB and SVM classifiers, FMMB achieved a competitive outcome. This method mitigates the curse of dimensionality and inspires the development of local-toglobal algorithms.

scholarly journals Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Pei Chen ◽  
Rui Liu ◽  
Kazuyuki Aihara ◽  
Luonan Chen
Keyword(s):  
Spatial Data ◽  
Target System ◽  
High Dimensional ◽  
Reservoir Computing ◽  
Computationally Efficient ◽  
Target Variable ◽  
Efficient Manner ◽  
Practical Applications ◽  
Spatiotemporal Information ◽  
Real World Datasets

Abstract We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network (ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short-term high-dimensional time series. Different from traditional reservoir computing whose reservoir is an external dynamical system irrelevant to the target system, ARNN directly transforms the observed high-dimensional dynamics as its reservoir, which maps the high-dimensional/spatial data to the future temporal values of a target variable based on our spatiotemporal information (STI) transformation. Thus, the multi-step prediction of the target variable is achieved in an accurate and computationally efficient manner. ARNN is successfully applied to both representative models and real-world datasets, all of which show satisfactory performance in the multi-step-ahead prediction, even when the data are perturbed by noise and when the system is time-varying. Actually, such ARNN transformation equivalently expands the sample size and thus has great potential in practical applications in artificial intelligence and machine learning.

Blocking Is Sensitive to Causal Structure in 4-Year-Old and 8-Year-Old Children

2005 ◽  
Vol 52 (4) ◽  
pp. 264-271 ◽  
Author(s):  
Tom Beckers ◽  
Uschi Van den Broeck ◽  
Marij Renne ◽  
Stefaan Vandorpe ◽  
Jan De Houwer ◽  
...  
Keyword(s):  
Young Children ◽  
Associative Learning ◽  
Learning Theory ◽  
Causal Structure ◽  
Age Groups ◽  
Learning Task ◽  
Contingency Learning

Abstract. In a contingency learning task, 4-year-old and 8-year-old children had to predict the outcome displayed on the back of a card on the basis of cues presented on the front. The task was embedded in either a causal or a merely predictive scenario. Within this task, either a forward blocking or a backward blocking procedure was implemented. Blocking occurred in the causal but not in the predictive scenario. Moreover, blocking was affected by the scenario to the same extent in both age groups. The pattern of results was similar for forward and backward blocking. These results suggest that even young children are sensitive to the causal structure of a contingency learning task and that the occurrence of blocking in such a task defies an explanation in terms of associative learning theory.

scholarly journals The rational use of causal inference to guide reinforcement learning strengthens with age

2019 ◽  
Author(s):  
Alexandra O. Cohen ◽  
Kate Nussenbaum ◽  
Hayley Dorfman ◽  
Samuel J. Gershman ◽  
Catherine A. Hartley
Keyword(s):  
Reinforcement Learning ◽  
Causal Structure ◽  
Learning Task ◽  
Negative Events ◽  
Shape Learning ◽  
Adolescents And Adults ◽  
Bayesian Reinforcement Learning ◽  
External Causes ◽  
Reinforcement Learning Models ◽  
Best Fit

Beliefs about the controllability of positive or negative events in the environment can shape learning throughout the lifespan. Previous research has shown that adults’ learning is modulated by beliefs about the causal structure of the environment such that they will update their value estimates to a lesser extent when the outcomes can be attributed to hidden causes. The present study examined whether external causes similarly influenced outcome attributions and learning across development. Ninety participants, ages 7 to 25 years, completed a reinforcement learning task in which they chose between two options with fixed reward probabilities. Choices were made in three distinct environments in which different hidden agents occasionally intervened to generate positive, negative, or random outcomes. Participants’ beliefs about hidden-agent intervention aligned well with the true probabilities of positive, negative, or random outcome manipulation in each of the three environments. Computational modeling of the learning data revealed that while the choices made by both adults (ages 18 - 25) and adolescents (ages 13 - 17) were best fit by Bayesian reinforcement learning models that incorporate beliefs about hidden agent intervention, those of children (ages 7 - 12) were best fit by a one learning rate model that updates value estimates based on choice outcomes alone. Together, these results suggest that while children demonstrate explicit awareness of the causal structure of the task environment they do not implicitly use beliefs about the causal structure of the environment to guide reinforcement learning in the same manner as adolescents and adults.

Feature Selection Algorithms for Mining High Dimensional DNA Microarray Data

2011 ◽  
pp. 685-710 ◽  
Author(s):  
David J. Dittman ◽  
Taghi M. Khoshgoftaar ◽  
Randall Wald ◽  
Jason Van Hulse
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Microarray Data ◽  
High Dimensional ◽  
Dna Microarray Data ◽  
Selection Algorithms

scholarly journals A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1132
Author(s):  
Deting Kong ◽  
Yuan Wang ◽  
Xinyan Wu ◽  
Xiyu Liu ◽  
Jianhua Qu ◽  
...  
Keyword(s):  
Dimensional Space ◽  
P Systems ◽  
High Dimensional ◽  
P System ◽  
Inhibitory Influence ◽  
Spiking Neural P Systems ◽  
Clustering Approach ◽  
Spatial Cluster Analysis ◽  
Effectiveness And Efficiency ◽  
Real World Datasets

In this paper, we propose a novel clustering approach based on P systems and grid- density strategy. We present grid-density based approach for clustering high dimensional data, which first projects the data patterns on a two-dimensional space to overcome the curse of dimensionality problem. Then, through meshing the plane with grid lines and deleting sparse grids, clusters are found out. In particular, we present weighted spiking neural P systems with anti-spikes and astrocyte (WSNPA2 in short) to implement grid-density based approach in parallel. Each neuron in weighted SN P system contains a spike, which can be expressed by a computable real number. Spikes and anti-spikes are inspired by neurons communicating through excitatory and inhibitory impulses. Astrocytes have excitatory and inhibitory influence on synapses. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.

scholarly journals The Inflation Technique for Causal Inference with Latent Variables

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Elie Wolfe ◽  
Robert W. Spekkens ◽  
Tobias Fritz
Keyword(s):  
Probability Distribution ◽  
Causal Inference ◽  
Latent Variables ◽  
Causal Model ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Difficult Case ◽  
Common Causes ◽  
Multiple Copies ◽  
New Inequalities

AbstractThe problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the constraints at the level of inflation are weak, such as observable statistical independences implied by disjoint causal ancestry, the translated constraints can be strong. We apply this method to derive new inequalities whose violation by a distribution witnesses that distribution’s incompatibility with the causal structure (of which Bell inequalities and Pearl’s instrumental inequality are prominent examples). We describe an algorithm for deriving all such inequalities for the original causal structure that follow from ancestral independences in the inflation. For three observed binary variables with pairwise common causes, it yields inequalities that are stronger in at least some aspects than those obtainable by existing methods. We also describe an algorithm that derives a weaker set of inequalities but is more efficient. Finally, we discuss which inflations are such that the inequalities one obtains from them remain valid even for quantum (and post-quantum) generalizations of the notion of a causal model.

scholarly journals Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

Molecules ◽  
2018 ◽  
Vol 23 (7) ◽  
pp. 1729
Author(s):  
Yinghan Hong ◽  
Zhifeng Hao ◽  
Guizhen Mai ◽  
Han Huang ◽  
Arun Kumar Sangaiah
Keyword(s):  
Real World ◽  
Data Science ◽  
Learning Algorithm ◽  
Causal Structure ◽  
Scientific Discovery ◽  
Causal Discovery ◽  
Causal Mechanism ◽  
Topological Order ◽  
Brain Storm Optimization ◽  
Real World Datasets

Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.

Improved IAMB with Expanded Markov Blanket for High-Dimensional Time Series Prediction

2016 ◽  
Vol 25 (2) ◽  
pp. 264-269 ◽  
Author(s):  
Xianglin Yang ◽  
Hong Yan ◽  
Hongbo Wang ◽  
Shaohua Tan ◽  
Yunhai Tong ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
High Dimensional ◽  
Markov Blanket
Sign in / Sign up

Export Citation Format

Share Document