From Ensemble Methods to Comprehensible Models
نویسندگان
چکیده
Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. However, there are two important shortcomings associated with ensemble methods. Huge amounts of memory are required to store a set of multiple hypotheses and, more importantly, comprehensibility of a single hypothesis is lost. In this work, we devise a new method to extract one single solution from a hypothesis ensemble without using extra data, based on two main ideas: the selected solution must be similar, semantically, to the combined solution, and this similarity is evaluated through the use of a random dataset. We have implemented the method using shared ensembles, because it allows for an exponential number of potential base hypotheses. We include several experiments showing that the new method selects a single hypothesis with an accuracy which is reasonably close to the combined hypothesis.
منابع مشابه
Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio
This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace...
متن کاملRule Extraction from Ensemble Methods Using Aggregated Decision Trees
Ensemble methods have become very well known for being powerful pattern recognition algorithms capable of achieving high accuracy. However, Ensemble methods produces learners that are not comprehensible or transferable thus making them unsuitable for tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensibl...
متن کاملSMILES: A Multi-purpose Learning System
A machine learning system is useful for extracting models from data that can be used for many applications such as data analysis , decision support or data mining. SMILES is a machine learning system that integrates many diierent features from other machine learning techniques and paradigms, and more importantly, it presents several innovations in almost all of these features, such as ensemble ...
متن کاملPresentation of new ensemble method of Bayesian and logistic regression models in landslide susceptibility assessment in the Khalkhal Township
The aim of current research is to assess of landslide susceptibility in the Khalkhal Township, southern Ardabil using an ensemble and new method namely Bayesian and logistic regression (BT-LR) models. At first, landslide inventory map was prepared and then effective factors on landslide occurrence were identified. These factors are slope degree, plan curvature, slope aspect, elevation, landuse,...
متن کاملShared Ensemble Learning Using Multi-trees
Decision tree learning is a machine learning technique that allows us to generate accurate and comprehensible models. Accuracy can be improved by ensemble methods which combine the predictions of a set of different trees. However, a large amount of resources is necessary to generate the ensemble. In this paper, we introduce a new ensemble method that minimises the usage of resources by sharing ...
متن کامل