Distributed Correlation-Based Feature Selection in Spark

CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop's MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.Comment: 25 pages, 5 figure

Hybrid feature selection technique for intrusion detection system

High dimensionality’s problems have make feature selection as one of the most important criteria in determining the efficiency of intrusion detection systems. In this study we have selected a hybrid feature selection model that potentially combines the strengths of both the filter and the wrapper selection procedure. The potential hybrid solution is expected to effectively select the optimal set of features in detecting intrusion. The proposed hybrid model was carried out using correlation feature selection (CFS) together with three different search techniques known as best-first, greedy stepwise and genetic algorithm. The wrapper-based subset evaluation uses a random forest (RF) classifier to evaluate each of the features that were first selected by the filter method. The reduced feature selection on both KDD99 and DARPA 1999 dataset was tested using RF algorithm with ten-fold cross-validation in a supervised environment. The experimental result shows that the hybrid feature selections had produced satisfactory outcome

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Prediction of protein-protein interaction types using association rule based classification

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Park et alBackground: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. Results: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content. Conclusion: The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/SHP was supported by the Korea Research Foundation Grant funded by the Korean Government(KRF-2005-214-E00050). JAR has been supported by the Programme Alβan, the European Union Programme of High level Scholarships for Latin America, scholarship E04D034854CL. SK was supported by Soongsil University Research Fund

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. To deal with this issue, filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers. This paper aims to provide a comprehensive review of different methods of feature selection presented for the tasks of multi-label classification. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically

Nuevos Modelos de Aprendizaje Híbrido para Clasificación y Ordenamiento Multi-Etiqueta

En la última década, el aprendizaje multi-etiqueta se ha convertido en una importante tarea de investigación, debido en gran parte al creciente número de problemas reales que contienen datos multi-etiqueta. En esta tesis se estudiaron dos problemas sobre datos multi-etiqueta, la mejora del rendimiento de los algoritmos en datos multi-etiqueta complejos y la mejora del rendimiento de los algoritmos a partir de datos no etiquetados. El primer problema fue tratado mediante métodos de estimación de atributos. Se evaluó la efectividad de los métodos de estimación de atributos propuestos en la mejora del rendimiento de los algoritmos de vecindad, mediante la parametrización de las funciones de distancias empleadas para recuperar los ejemplos más cercanos. Además, se demostró la efectividad de los métodos de estimación en la tarea de selección de atributos. Por otra parte, se desarrolló un algoritmo de vecindad inspirado en el enfoque de clasifcación basada en gravitación de datos. Este algoritmo garantiza un balance adecuado entre eficiencia y efectividad en su solución ante datos multi-etiqueta complejos. El segundo problema fue resuelto mediante técnicas de aprendizaje activo, lo cual permite reducir los costos del etiquetado de datos y del entrenamiento de un mejor modelo. Se propusieron dos estrategias de aprendizaje activo. La primer estrategia resuelve el problema de aprendizaje activo multi-etiqueta de una manera efectiva y eficiente, para ello se combinaron dos medidas que representan la utilidad de un ejemplo no etiquetado. La segunda estrategia propuesta se enfocó en la resolución del problema de aprendizaje activo multi-etiqueta en modo de lotes, para ello se formuló un problema multi-objetivo donde se optimizan tres medidas, y el problema de optimización planteado se resolvió mediante un algoritmo evolutivo. Como resultados complementarios derivados de esta tesis, se desarrolló una herramienta computacional que favorece la implementación de métodos de aprendizaje activo y la experimentación en esta tarea de estudio. Además, se propusieron dos aproximaciones que permiten evaluar el rendimiento de las técnicas de aprendizaje activo de una manera más adecuada y robusta que la empleada comunmente en la literatura. Todos los métodos propuestos en esta tesis han sido evaluados en un marco experimental adecuado, se utilizaron numerosos conjuntos de datos y se compararon los rendimientos de los algoritmos frente a otros métodos del estado del arte. Los resultados obtenidos, los cuales fueron verificados mediante la aplicación de test estadísticos no paramétricos, demuestran la efectividad de los métodos propuestos y de esta manera comprueban las hipótesis planteadas en esta tesis.In the last decade, multi-label learning has become an important area of research due to the large number of real-world problems that contain multi-label data. This doctoral thesis is focused on the multi-label learning paradigm. Two problems were studied, rstly, improving the performance of the algorithms on complex multi-label data, and secondly, improving the performance through unlabeled data. The rst problem was solved by means of feature estimation methods. The e ectiveness of the feature estimation methods proposed was evaluated by improving the performance of multi-label lazy algorithms. The parametrization of the distance functions with a weight vector allowed to recover examples with relevant label sets for classi cation. It was also demonstrated the e ectiveness of the feature estimation methods in the feature selection task. On the other hand, a lazy algorithm based on a data gravitation model was proposed. This lazy algorithm has a good trade-o between e ectiveness and e ciency in the resolution of the multi-label lazy learning. The second problem was solved by means of active learning techniques. The active learning methods allowed to reduce the costs of the data labeling process and training an accurate model. Two active learning strategies were proposed. The rst strategy e ectively solves the multi-label active learning problem. In this strategy, two measures that represent the utility of an unlabeled example were de ned and combined. On the other hand, the second active learning strategy proposed resolves the batch-mode active learning problem, where the aim is to select a batch of unlabeled examples that are informative and the information redundancy is minimal. The batch-mode active learning was formulated as a multi-objective problem, where three measures were optimized. The multi-objective problem was solved through an evolutionary algorithm. This thesis also derived in the creation of a computational framework to develop any active learning method and to favor the experimentation process in the active learning area. On the other hand, a methodology based on non-parametric tests that allows a more adequate evaluation of active learning performance was proposed. All methods proposed were evaluated by means of extensive and adequate experimental studies. Several multi-label datasets from di erent domains were used, and the methods were compared to the most signi cant state-of-the-art algorithms. The results were validated using non-parametric statistical tests. The evidence showed the e ectiveness of the methods proposed, proving the hypotheses formulated at the beginning of this thesis