2,271 research outputs found
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
Gene function finding through cross-organism ensemble learning
Background: Structured biological information about genes and proteins is a valuable resource to improve discovery and understanding of complex biological processes via machine learning algorithms. Gene Ontology (GO) controlled annotations describe, in a structured form, features and functions of genes and proteins of many organisms. However, such valuable annotations are not always reliable and sometimes are incomplete, especially for rarely studied organisms. Here, we present GeFF (Gene Function Finder), a novel cross-organism ensemble learning method able to reliably predict new GO annotations of a target organism from GO annotations of another source organism evolutionarily related and better studied. Results: Using a supervised method, GeFF predicts unknown annotations from random perturbations of existing annotations. The perturbation consists in randomly deleting a fraction of known annotations in order to produce a reduced annotation set. The key idea is to train a supervised machine learning algorithm with the reduced annotation set to predict, namely to rebuild, the original annotations. The resulting prediction model, in addition to accurately rebuilding the original known annotations for an organism from their perturbed version, also effectively predicts new unknown annotations for the organism. Moreover, the prediction model is also able to discover new unknown annotations in different target organisms without retraining.We combined our novel method with different ensemble learning approaches and compared them to each other and to an equivalent single model technique. We tested the method with five different organisms using their GO annotations: Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum. The outcomes demonstrate the effectiveness of the cross-organism ensemble approach, which can be customized with a trade-off between the desired number of predicted new annotations and their precision.A Web application to browse both input annotations used and predicted ones, choosing the ensemble prediction method to use, is publicly available at http://tiny.cc/geff/. Conclusions: Our novel cross-organism ensemble learning method provides reliable predicted novel gene annotations, i.e., functions, ranked according to an associated likelihood value. They are very valuable both to speed the annotation curation, focusing it on the prioritized new annotations predicted, and to complement known annotations available
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Supervised cross-modal factor analysis for multiple modal data classification
In this paper we study the problem of learning from multiple modal data for
purpose of document classification. In this problem, each document is composed
two different modals of data, i.e., an image and a text. Cross-modal factor
analysis (CFA) has been proposed to project the two different modals of data to
a shared data space, so that the classification of a image or a text can be
performed directly in this space. A disadvantage of CFA is that it has ignored
the supervision information. In this paper, we improve CFA by incorporating the
supervision information to represent and classify both image and text modals of
documents. We project both image and text data to a shared data space by factor
analysis, and then train a class label predictor in the shared space to use the
class label information. The factor analysis parameter and the predictor
parameter are learned jointly by solving one single objective function. With
this objective function, we minimize the distance between the projections of
image and text of the same document, and the classification error of the
projection measured by hinge loss function. The objective function is optimized
by an alternate optimization strategy in an iterative algorithm. Experiments in
two different multiple modal document data sets show the advantage of the
proposed algorithm over other CFA methods
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease
Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and
associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be
used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled
samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active
learning. This is achieved through selective query of challenging samples for labeling. To the best of our
knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of
Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers
determine whether a patient’s three main coronary arteries are stenotic or not. The fourth classifier predicts
whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the
outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled
samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The
training is performed once more using the samples labeled so far. The interleaved phases of labeling and training
are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined
with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is
justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of
dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis
of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is
presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample
discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the
three main coronary arteries as a sample label and considering the two remaining arteries as sample features
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