A novel mutual dependence measure in structure learning

Journal of Intelligent &Amp; Fuzzy Systems

Asghar

2014

Self Cite

Bayesian Belief Network has inspired the community of machine learning in the domain of structure learning. Numerous scoring functions have been introduced in the structure learning. The performance of these scoring functions usually tends to favor the dense Bayesian Network by using an implicit over-fitting phenomenon. Motivated by this limitation, this study has introduced a novel scoring function which is optimized for producing non complex learnt structures but with the target of higher classification accuracy. The introduced scoring function assumes no external parameter for its fine tuning. It is decomposable and possesses no penalty factor. The introduced scoring function holds its mathematical interpretation within information theory. This scoring function is designed to maximize the discriminant function for given dataset query variables with respect to the class feature and other non class features. The empirical evaluation of the introduced scoring function points out that its classification accuracy is significantly better than other existent scoring functions using greedy search algorithm K2 and hill climber. The outcome of this study illustrates that simplistic structure with higher classification performance is possible by means of exercising the proposed scoring function in the Bayesian Belief structure learning.

Section: Definitionmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Structure learning via non-parametric factorized joint likelihood function

Journal of Intelligent &Amp; Fuzzy Systems

Asghar

2014

Self Cite

“…They classified the techniques according to suitability, variety, usage and potential to sequence analysis and micorarray analysis. Although the pool of FSS techniques is becoming larger and larger [4,[10][11]; nevertheless specific exhaustive review leading to a wealth of comparative report for Bayesian belief network's various scoring function is not addressed so far. We in this study have incremented useful information in these survey reports; moreover our analysis is more precise in tweaking BBN in particular.…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Analysis and Performance Evaluation of Feature Selection Techniques for Belief Network Classification System

2015

IJCA

Self Cite

In the last two decades, there has been significant advancement in heuristics for inducing Bayesian belief networks for the purpose of automatic distillation of knowledge from masses of data with target concepts. However, there are various circumstances where we are confronted to fix a set of most influencing variables in modeling of class variable. This arises in provision of confidence measures on set of variables used in the structure learning of data. In this study, we have tweaked empirical as well as theoretical aspects of various feature selection evaluators, their corresponding searching methods under six well known scoring functions in K2 which is a notable structure learning technique in Bayesian belief network. We have come up with some useful findings for overall computationally efficient approach among eleven evaluators. This analysis is useful in inducing better structure from the given dataset in imparting improved performance metric useful in the domain of control and automation.

“…This was shown by an earlier version of the proposed discriminant function in which we highlight that majority of the discriminant functions can't precisely capture the casual relationship between two variables in pursuit of true topology in numerous situations; this ultimately leads to the selection of potential neighbor and parents becoming unreasonable. However Integration to Segregation (I2S) is capable of rightly identify it in majority of the cases as compared to BIC, MDL, BDeu, Entropy and many more [26], [27]. Moreover, Naeem et al [26], [27] described that a structure in which class node is placed at the top most may lead to higher predictive accuracies.…”

Section: Letmentioning

confidence: 99%

A Parameter Free BBN Discriminant Function for Optimum Model Complexity versus Goodness of Data Fitting

Journal of Applied Research and Technology

Asghar

2014

Bayesian Belief Network (BBN) is an appealing classification model for learning causal and noncausal dependencies among a set of query variables. It is a challenging task to learning BBN structure from observational data because of pool of large number of candidate network structures. In this study, we have addressed the issue of goodness of data fitting versus model complexity. While doing so, we have proposed discriminant function which is non-parametric, free of implicit assumptions but delivering better classification accuracy in structure learning. The contribution in this study is twofold, first contribution (discriminant function) is in BBN structure learning and second contribution is for Decision Stump classifier. While designing the novel discriminant function, we analyzed the underlying relationship between the characteristics of data and accuracy of decision stump classifier. We introduced a meta characteristic measure AMfDS (herein known as Affinity Metric for Decision Stump) which is quite useful in prediction of classification accuracy of Decision Stump. AMfDS requires a single scan of the dataset.