4,706 research outputs found
Massively-Parallel Feature Selection for Big Data
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for
feature selection (FS) in Big Data settings (high dimensionality and/or sample
size). To tackle the challenges of Big Data FS PFBP partitions the data matrix
both in terms of rows (samples, training examples) as well as columns
(features). By employing the concepts of -values of conditional independence
tests and meta-analysis techniques PFBP manages to rely only on computations
local to a partition while minimizing communication costs. Then, it employs
powerful and safe (asymptotically sound) heuristics to make early, approximate
decisions, such as Early Dropping of features from consideration in subsequent
iterations, Early Stopping of consideration of features within the same
iteration, or Early Return of the winner in each iteration. PFBP provides
asymptotic guarantees of optimality for data distributions faithfully
representable by a causal network (Bayesian network or maximal ancestral
graph). Our empirical analysis confirms a super-linear speedup of the algorithm
with increasing sample size, linear scalability with respect to the number of
features and processing cores, while dominating other competitive algorithms in
its class
Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care
We present an ensemble learning method that predicts large increases in the
hours of home care received by citizens. The method is supervised, and uses
different ensembles of either linear (logistic regression) or non-linear
(random forests) classifiers. Experiments with data available from 2013 to 2017
for every citizen in Copenhagen receiving home care (27,775 citizens) show that
prediction can achieve state of the art performance as reported in similar
health related domains (AUC=0.715). We further find that competitive results
can be obtained by using limited information for training, which is very useful
when full records are not accessible or available. Smart city analytics does
not necessarily require full city records.
To our knowledge this preliminary study is the first to predict large
increases in home care for smart city analytics
Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry
Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results
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