28 research outputs found
Conformal Prediction with Partially Labeled Data
While the predictions produced by conformal prediction are set-valued, the
data used for training and calibration is supposed to be precise. In the
setting of superset learning or learning from partial labels, a variant of
weakly supervised learning, it is exactly the other way around: training data
is possibly imprecise (set-valued), but the model induced from this data yields
precise predictions. In this paper, we combine the two settings by making
conformal prediction amenable to set-valued training data. We propose a
generalization of the conformal prediction procedure that can be applied to
set-valued training and calibration data. We prove the validity of the proposed
method and present experimental studies in which it compares favorably to
natural baselines
A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.
We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing 'slide-level' diagnosis by using individual 'cell-level' diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform
Decision rules, trees and tests for tables with many-valued decisions : comparative study
In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches
Decomposition-based Generation Process for Instance-Dependent Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning problem,
where each training example is associated with a set of candidate labels among
which only one is true. Most existing PLL approaches assume that the incorrect
labels in each training example are randomly picked as the candidate labels and
model the generation process of the candidate labels in a simple way. However,
these approaches usually do not perform as well as expected due to the fact
that the generation process of the candidate labels is always
instance-dependent. Therefore, it deserves to be modeled in a refined way. In
this paper, we consider instance-dependent PLL and assume that the generation
process of the candidate labels could decompose into two sequential parts,
where the correct label emerges first in the mind of the annotator but then the
incorrect labels related to the feature are also selected with the correct
label as candidate labels due to uncertainty of labeling. Motivated by this
consideration, we propose a novel PLL method that performs Maximum A
Posterior(MAP) based on an explicitly modeled generation process of candidate
labels via decomposed probability distribution models. Experiments on benchmark
and real-world datasets validate the effectiveness of the proposed method
Partial Label Learning with Self-Guided Retraining
Partial label learning deals with the problem where each training instance is
assigned a set of candidate labels, only one of which is correct. This paper
provides the first attempt to leverage the idea of self-training for dealing
with partially labeled examples. Specifically, we propose a unified formulation
with proper constraints to train the desired model and perform pseudo-labeling
jointly. For pseudo-labeling, unlike traditional self-training that manually
differentiates the ground-truth label with enough high confidence, we introduce
the maximum infinity norm regularization on the modeling outputs to
automatically achieve this consideratum, which results in a convex-concave
optimization problem. We show that optimizing this convex-concave problem is
equivalent to solving a set of quadratic programming (QP) problems. By
proposing an upper-bound surrogate objective function, we turn to solving only
one QP problem for improving the optimization efficiency. Extensive experiments
on synthesized and real-world datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art partial label learning
approaches.Comment: 8 pages, accepted by AAAI-1
Semi-supervised cross-entropy clustering with information bottleneck constraint
In this paper, we propose a semi-supervised clustering method, CEC-IB, that
models data with a set of Gaussian distributions and that retrieves clusters
based on a partial labeling provided by the user (partition-level side
information). By combining the ideas from cross-entropy clustering (CEC) with
those from the information bottleneck method (IB), our method trades between
three conflicting goals: the accuracy with which the data set is modeled, the
simplicity of the model, and the consistency of the clustering with side
information. Experiments demonstrate that CEC-IB has a performance comparable
to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but
is faster, more robust to noisy labels, automatically determines the optimal
number of clusters, and performs well when not all classes are present in the
side information. Moreover, in contrast to other semi-supervised models, it can
be successfully applied in discovering natural subgroups if the partition-level
side information is derived from the top levels of a hierarchical clustering
General Partial Label Learning via Dual Bipartite Graph Autoencoder
We formulate a practical yet challenging problem: General Partial Label
Learning (GPLL). Compared to the traditional Partial Label Learning (PLL)
problem, GPLL relaxes the supervision assumption from instance-level --- a
label set partially labels an instance --- to group-level: 1) a label set
partially labels a group of instances, where the within-group instance-label
link annotations are missing, and 2) cross-group links are allowed ---
instances in a group may be partially linked to the label set from another
group. Such ambiguous group-level supervision is more practical in real-world
scenarios as additional annotation on the instance-level is no longer required,
e.g., face-naming in videos where the group consists of faces in a frame,
labeled by a name set in the corresponding caption. In this paper, we propose a
novel graph convolutional network (GCN) called Dual Bipartite Graph Autoencoder
(DB-GAE) to tackle the label ambiguity challenge of GPLL. First, we exploit the
cross-group correlations to represent the instance groups as dual bipartite
graphs: within-group and cross-group, which reciprocally complements each other
to resolve the linking ambiguities. Second, we design a GCN autoencoder to
encode and decode them, where the decodings are considered as the refined
results. It is worth noting that DB-GAE is self-supervised and transductive, as
it only uses the group-level supervision without a separate offline training
stage. Extensive experiments on two real-world datasets demonstrate that DB-GAE
significantly outperforms the best baseline over absolute 0.159 F1-score and
24.8% accuracy. We further offer analysis on various levels of label
ambiguities.Comment: 8 page