An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.<br /

Supervised And Semi-supervised Learning Using Informative Feature Subspaces

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2010Web madenciliği, biyoinformatik ve konuşma tanıma gibi birçok farklı alanda çok yüksek miktarda etiketsiz veri ve farklı öznitelik uzayları bulunmaktadır. Birlikte öğrenme (Co-training) algoritması gibi yarı-eğitmenli algoritmalar etiketsiz verinin kullanımını amaçlamaktadır. Rastgele öznitelik alt uzayları (RAS) metodu farklı öznitelik alt uzaylarını kullanarak sınıflandırıcı eğitmeyi ve bu sınıflandırıcıları, topluluklarda birleştirmeyi amaçlamaktadır. Bu tez çalışmasında, sınıflandırıcı toplulukları için ilişkili öznitelik alt uzayları rastgele seçilerek; bilgi içeren ve çeşitliliği sağlanmış öznitelik alt uzaylarının oluşturulması sağlanmıştır. Oluşturulan sınıflandırıcı toplulukları, eğitmenli ve yarı-eğitmenli öğrenme için kullanılmıştır. Önerdiğimiz ilk yöntem, öznitelik alt uzaylarını karşılıklı bilgi miktarına bağlı ilişki değerlerini kullanarak seçmektedir. Bu yöntem Rel-RAS (eğitmenli) ve Rel-RASCO (yarı-eğitmenli) algoritmalarında kullanılmıştır. İkinci yöntem, ilişkili ve artık olmayan öznitelik alt uzaylarını seçmek için, mRMR (en düşük artıklık ve en yüksek ilişkili) öznitelik seçme algoritmasının değiştirilmiş şeklini kullanmaktadır. Bu yöntem mRMR-RAS (eğitmenli) ve mRMR-RASCO (yarı-eğitmenli) algoritmalarında kullanılmıştır. Önerilen yöntemlerin deneysel analizleri belirli sayıda veri kümesinde gerçekleştirilmiş ve mevcut yöntemlerle karşılaştırılmıştır. Aynı zamanda önerilen yöntemlerle oluşturulmuş sınıflandırıcı topluluklarının teorik analizleri; Kohavi Wolpert (KW) varyans, bilgi kuramı tabanlı düşük düzeyli çeşitlilik (LOD) ve bilgi kuramı sayısı (ITS) kullanılarak gerçekleştirilmiştir. LOD ve KW-varyansının davranışları arasında benzerlik bulunmuş ve topluluk sınıflandırma başarımının ITS ile açıklanabileceği görülmüştür.In many different fields, such as web mining, bioinformatics, speech recognition, there is an abundance of unlabeled data and different feature views. Semi-supervised learning algorithms such as Co-training aim to make use of unlabeled data. Random (feature) subspace (RAS) methods aim to use different feature subspaces to train different classifiers and combine them in an ensemble. In this thesis, we obtain informative and diverse feature subspaces for classifier ensembles by means of randomly drawing relevant feature subspaces. We then use these ensembles for supervised and semi-supervised learning. Our first algorithm produces relevant random subspaces using the mutual information based relevance values. This method is used in Rel-RAS (supervised) and Rel-RASCO (semi-supervised) algorithms. The second algorithm modifies the mRMR (Minimum Redundancy Maximum Relevance) feature selection algorithm to produce random feature subsets that are both relevant and non-redundant. This method is used in mRMR-RAS (supervised) and mRMR-RASCO (semi-supervised) algorithms. We perform experimental analysis of our methods on a number of datasets and compare them to existing methods. We also do theoretical analysis of classifier ensembles produced by our methods using Kohavi Wolpert (KW) variance, information theory based low order diversity (LOD) and information theoretic scores (ITS). We find out that LOD has a similar tendency with KW-variance and ensemble accuracy of the algorithms can be explained using ITS.DoktoraPh

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings