Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators

In this paper we present some preliminary work towards the development of a new evolutionary subsampling technique for solving the non-intrusive load monitoring (NILM) problem. The NILM problem concerns using predictive algorithms to analyse whole-house energy usage measurements, so that individual appliance energy usages can be disaggregated. The motivation is to educate home owners about their energy usage. However, by their very nature, the datasets used in this research are massively imbalanced in their target value distributions. Consequently standard machine learning techniques, which often rely on optimising for root mean squared error (RMSE), typically fail. We therefore propose the target-weighted RMSE (TW-RMSE) metric as an alternative fitness function for optimising load disaggregators, and show in a simple initial study in which random search is utilised that TW-RMSE is a metric that can be optimised, and therefore has the potential to be included in a larger evolutionary subsampling-based solution to this problem

Subgroup Analysis via Recursive Partitioning

Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect on a response is of central interest. Its goal is to determine the heterogeneity of the treatment effect across subpopulations. In this paper, we adapt the idea of recursive partitioning and introduce an interaction tree (IT) procedure to conduct subgroup analysis. The IT procedure automatically facilitates a number of objectively defined subgroups, in some of which the treatment effect is found prominent while in others the treatment has a negligible or even negative effect. The standard CART (Breiman et al., 1984) methodology is inherited to construct the tree structure. Also, in order to extract factors that contribute to the heterogeneity of the treatment effect, variable importance measure is made available via random forests of the interaction trees. Both simulated experiments and analysis of census wage data are presented for illustration.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000270824200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Automation & Control SystemsComputer Science, Artificial IntelligenceSCI(E)EI38ARTICLE141-1581

Shapley-Value Data Valuation for Semi-supervised Learning

Semi-supervised learning aims at training accurate prediction models on labeled and unlabeled data. Its realization strongly depends on selecting pseudo-labeled data. The standard approach is to select instances based on the pseudo-label confidence values that they receive from the prediction models. In this paper we argue that this is an indirect approach w.r.t. the main goal of semi-supervised learning. Instead, we propose a direct approach that selects the pseudo-labeled instances based on their individual contributions for the performance of the prediction models. The individual instance contributions are computed as Shapley values w.r.t. characteristic functions related to the model performance. Experiments show that our approach outperforms the standard one when used in semi-supervised wrappers