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Multi-class protein fold classification using a new ensemble machine learning approach.
Protein structure classification represents an important process in understanding the associations
between sequence and structure as well as possible functional and evolutionary relationships.
Recent structural genomics initiatives and other high-throughput experiments have populated the
biological databases at a rapid pace. The amount of structural data has made traditional methods
such as manual inspection of the protein structure become impossible. Machine learning has been
widely applied to bioinformatics and has gained a lot of success in this research area. This work
proposes a novel ensemble machine learning method that improves the coverage of the classifiers
under the multi-class imbalanced sample sets by integrating knowledge induced from different base
classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have
compared our approach with PART and show that our method improves the sensitivity of the
classifier in protein fold classification. Furthermore, we have extended this method to learning over
multiple data types, preserving the independence of their corresponding data sources, and show
that our new approach performs at least as well as the traditional technique over a single joined
data source. These experimental results are encouraging, and can be applied to other bioinformatics
problems similarly characterised by multi-class imbalanced data sets held in multiple data
sources
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
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Class imbalance classification is a challenging research problem in data
mining and machine learning, as most of the real-life datasets are often
imbalanced in nature. Existing learning algorithms maximise the classification
accuracy by correctly classifying the majority class, but misclassify the
minority class. However, the minority class instances are representing the
concept with greater interest than the majority class instances in real-life
applications. Recently, several techniques based on sampling methods
(under-sampling of the majority class and over-sampling the minority class),
cost-sensitive learning methods, and ensemble learning have been used in the
literature for classifying imbalanced datasets. In this paper, we introduce a
new clustering-based under-sampling approach with boosting (AdaBoost)
algorithm, called CUSBoost, for effective imbalanced classification. The
proposed algorithm provides an alternative to RUSBoost (random under-sampling
with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost)
algorithms. We evaluated the performance of CUSBoost algorithm with the
state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost,
SMOTEBoost on 13 imbalance binary and multi-class datasets with various
imbalance ratios. The experimental results show that the CUSBoost is a
promising and effective approach for dealing with highly imbalanced datasets.Comment: CSITSS-201
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