One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm

Building Gene Expression Profile Classifiers with a Simple and Efficient Rejection Option in R

Background: The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers. Results: This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention. Conclusions: This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be availabl

Joint sub-classifiers one class classification model for avian influenza outbreak detection

H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset. © 2011 Imperial College Press

Random Subspace Learning on Outlier Detection and Classification with Minimum Covariance Determinant Estimator

The questions brought by high dimensional data is interesting and challenging. Our study is targeting on the particular type of data in this situation that namely “large p, small n”. Since the dimensionality is massively larger than the number of observations in the data, any measurement of covariance and its inverse will be miserably affected. The definition of high dimension in statistics has been changed throughout decades. Modern datasets with over thousands of dimensions are demanding the ability to gain deeper understanding but hindered by the curse of dimensionality. We decide to review and explore further to negotiate with the curse and extend previous studies to pave a new way for estimating robustness then apply it to outlier detection and classification. We explored the random subspace learning and expand other classification and outlier detection algorithms to adapt its framework. Our proposed methods can handle both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned

Fault analysis using state-of-the-art classifiers

Fault Analysis is the detection and diagnosis of malfunction in machine operation or process control. Early fault analysis techniques were reserved for high critical plants such as nuclear or chemical industries where abnormal event prevention is given utmost importance. The techniques developed were a result of decades of technical research and models based on extensive characterization of equipment behavior. This requires in-depth knowledge of the system and expert analysis to apply these methods for the application at hand. Since machine learning algorithms depend on past process data for creating a system model, a generic autonomous diagnostic system can be developed which can be used for application in common industrial setups. In this thesis, we look into some of the techniques used for fault detection and diagnosis multi-class and one-class classifiers. First we study Feature Selection techniques and the classifier performance is analyzed against the number of selected features. The aim of feature selection is to reduce the impact of irrelevant variables and to reduce computation burden on the learning algorithm. We introduce the feature selection algorithms as a literature survey. Only few algorithms are implemented to obtain the results. Fault data from a Radio Frequency (RF) generator is used to perform fault detection and diagnosis. Comparison between continuous and discrete fault data is conducted for the Support Vector Machines (SVM) and Radial Basis Function Network (RBF) classifiers. In the second part we look into one-class classification techniques and their application to fault detection. One-class techniques were primarily developed to identify one class of objects from all other possible objects. Since all fault occurrences in a system cannot be simulated or recorded, one-class techniques help in identifying abnormal events. We introduce four one-class classifiers and analyze them using Receiver-Operating Characteristic (ROC) curve. We also develop a feature extraction method for the RF generator data which is used to obtain results for one-class classifiers and Radial Basis Function Network two class classification. To apply these techniques for real-time verification, the RIT Fault Prediction software is built. LabView environment is used to build a basic data management and fault detection using Radial Basis Function Network. This software is stand alone and acts as foundation for future implementations

Assessing similarity of feature selection techniques in high-dimensional domains

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement