47 research outputs found
Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has
been recently extended to exploit prior knowledge on the cardinality of each
cluster. Such knowledge is used to increase performance as well as solution
quality. In this paper, we propose a global optimization approach based on the
branch-and-cut technique to solve the cardinality-constrained MSSC. For the
lower bound routine, we use the semidefinite programming (SDP) relaxation
recently proposed by Rujeerapaiboon et al. [SIAM J. Optim. 29(2), 1211-1239,
(2019)]. However, this relaxation can be used in a branch-and-cut method only
for small-size instances. Therefore, we derive a new SDP relaxation that scales
better with the instance size and the number of clusters. In both cases, we
strengthen the bound by adding polyhedral cuts. Benefiting from a tailored
branching strategy which enforces pairwise constraints, we reduce the
complexity of the problems arising in the children nodes. For the upper bound,
instead, we present a local search procedure that exploits the solution of the
SDP relaxation solved at each node. Computational results show that the
proposed algorithm globally solves, for the first time, real-world instances of
size 10 times larger than those solved by state-of-the-art exact methods
A machine learning approach for forecasting hierarchical time series
In this paper, we propose a machine learning approach for forecasting
hierarchical time series. When dealing with hierarchical time series, apart
from generating accurate forecasts, one needs to select a suitable method for
producing reconciled forecasts. Forecast reconciliation is the process of
adjusting forecasts to make them coherent across the hierarchy. In literature,
coherence is often enforced by using a post-processing technique on the base
forecasts produced by suitable time series forecasting methods. On the
contrary, our idea is to use a deep neural network to directly produce accurate
and reconciled forecasts. We exploit the ability of a deep neural network to
extract information capturing the structure of the hierarchy. We impose the
reconciliation at training time by minimizing a customized loss function. In
many practical applications, besides time series data, hierarchical time series
include explanatory variables that are beneficial for increasing the
forecasting accuracy. Exploiting this further information, our approach links
the relationship between time series features extracted at any level of the
hierarchy and the explanatory variables into an end-to-end neural network
providing accurate and reconciled point forecasts. The effectiveness of the
approach is validated on three real-world datasets, where our method
outperforms state-of-the-art competitors in hierarchical forecasting
Supervised Feature Compression based on Counterfactual Analysis
Counterfactual Explanations are becoming a de-facto standard in post-hoc
interpretable machine learning. For a given classifier and an instance
classified in an undesired class, its counterfactual explanation corresponds to
small perturbations of that instance that allows changing the classification
outcome. This work aims to leverage Counterfactual Explanations to detect the
important decision boundaries of a pre-trained black-box model. This
information is used to build a supervised discretization of the features in the
dataset with a tunable granularity. Using the discretized dataset, a smaller,
therefore more interpretable Decision Tree can be trained, which, in addition,
enhances the stability and robustness of the baseline Decision Tree. Numerical
results on real-world datasets show the effectiveness of the approach in terms
of accuracy and sparsity compared to the baseline Decision Tree.Comment: 29 pages, 12 figure
An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is
traditionally considered an unsupervised learning task. In recent years, the
use of background knowledge to improve the cluster quality and promote
interpretability of the clustering process has become a hot research topic at
the intersection of mathematical optimization and machine learning research.
The problem of taking advantage of background information in data clustering is
called semi-supervised or constrained clustering. In this paper, we present a
branch-and-cut algorithm for semi-supervised MSSC, where background knowledge
is incorporated as pairwise must-link and cannot-link constraints. For the
lower bound procedure, we solve the semidefinite programming relaxation of the
MSSC discrete optimization model, and we use a cutting-plane procedure for
strengthening the bound. For the upper bound, instead, by using integer
programming tools, we use an adaptation of the k-means algorithm to the
constrained case. For the first time, the proposed global optimization
algorithm efficiently manages to solve real-world instances up to 800 data
points with different combinations of must-link and cannot-link constraints and
with a generic number of features. This problem size is about four times larger
than the one of the instances solved by state-of-the-art exact algorithms
Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition
: The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics
Improving P300 Speller performance by means of optimization and machine learning
Brain-Computer Interfaces (BCIs) are systems allowing people to interact with
the environment bypassing the natural neuromuscular and hormonal outputs of the
peripheral nervous system (PNS). These interfaces record a user's brain
activity and translate it into control commands for external devices, thus
providing the PNS with additional artificial outputs. In this framework, the
BCIs based on the P300 Event-Related Potentials (ERP), which represent the
electrical responses recorded from the brain after specific events or stimuli,
have proven to be particularly successful and robust. The presence or the
absence of a P300 evoked potential within the EEG features is determined
through a classification algorithm. Linear classifiers such as SWLDA and SVM
are the most used for ERPs' classification. Due to the low signal-to-noise
ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations)
are carried out and then averaged before the signals being classified. However,
while augmenting the number of iterations improves the Signal-to-Noise Ratio
(SNR), it also slows down the process. In the early studies, the number of
iterations was fixed (no stopping), but recently, several early stopping
strategies have been proposed in the literature to dynamically interrupt the
stimulation sequence when a certain criterion is met to enhance the
communication rate. In this work, we explore how to improve the classification
performances in P300 based BCIs by combining optimization and machine learning.
First, we propose a new decision function that aims at improving classification
performances in terms of accuracy and Information Transfer Rate both in a no
stopping and early stopping environment. Then, we propose a new SVM training
problem that aims to facilitate the target-detection process. Our approach
proves to be effective on several publicly available datasets.Comment: 32 pages, research articl
A game-theoretic approach to computation offloading in mobile cloud computing
We consider a three-tier architecture for mobile and pervasive computing
scenarios, consisting of a local tier ofmobile nodes, a middle tier (cloudlets) of nearby
computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have
practically infinite resources. This architecture has been proposed to get the benefits of
computation offloading from mobile nodes to external servers while limiting the use
of distant servers whose higher latency could negatively impact the user experience.
For this architecture, we consider a usage scenario where no central authority exists
and multiple non-cooperative mobile users share the limited computing resources of
a close-by cloudlet and can selfishly decide to send their computations to any of the
three tiers. We define a model to capture the users interaction and to investigate the
effects of computation offloading on the users’ perceived performance. We formulate
the problem as a generalized Nash equilibrium problem and show existence of an
equilibrium.We present a distributed algorithm for the computation of an equilibrium
which is tailored to the problem structure and is based on an in-depth analysis of
the underlying equilibrium problem. Through numerical examples, we illustrate its
behavior and the characteristics of the achieved equilibria