An extended negative selection algorithm for anomaly detection

This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which do not make use of non-self data, the extended negative selection algorithm first acquires prior knowledge about the characteristics of the Problem space from the historial sample data by using machine learning techniques. Such data consists of both self data and non-self data. The acquired prior knowledge is represented in the form of production rules and thus viewed as common schemata which characterise the two subspaces: self-subspace and non-self-subspace, and provide important information to the generation of detection rules. One advantage of our approach is that it does not rely on the structured representation of the data and can be applied to general anomaly detection. To test the effectiveness, we test our approach through experiments with the public data set iris and KDDrsquo99 published data set.

Applying both positive and negative selection to supervised learning for anomaly detection

This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the normal class via co-evolutionary genetic algorithm, which is inspired from the positive selection, and then generates synthetic samples of the anomaly class, which is based on the negative selection in the immune system. Two algorithms about synthetic generation of the anomaly class are proposed. One deals with data sets containing a few anomalous samples; while the other deals with data sets containing no anomalous samples at all. The experimental results on some benchmark data sets from UCI data set repertory show that the detection rate is improved evidently, accompanied by a slight increase in false alarm rate via introducing novel synthetic samples of the anomaly class. The advantages of our method are the increased ability of classifiers in identifying both previously known and innovative anomalies, and the maximal degradation of overfitting phenomenon.

Constructing detectors in schema complementary space for anomaly detection

This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which directly construct detectors in the complementary space of self-data space, our approach first evolves a number of common schemata through coevolutionary genetic algorithm in self-data space, and then constructs detectors in the complementary space of the schemata. These common schemata characterize self-data space and thus guide the generation of detection rules. By converting data space into schema space, we can efficiently generate an appropriate number of detectors with diversity for anomaly detection. The approach is tested for its effectiveness through experiment with the published data set iris.

An immunological approach to raising alarms in video surveillance

Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three detectors for detecting abnormal activity are generated using negative selection. Tracks gathered by people’s movements in a room are used for experimentation and results have shown that the classifier is able to discriminate abnormal from normal activities in terms of both trajectory and time spent at a location.

An immunological approach to raising alarms in video surveillance

Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three types of detectors for detecting abnormal activity are developed using negative selection. Tracks gathered by people's movements in a room are used for experimentation and results have shown that the classifier is able to discriminate abnormal from normal activities in terms of both trajectory and time spent at a location.

Correlation structure and model selection for negative binomial distribution in GEE

A new model selection criterion, termed as the “quasi-likelihood under the independence model criterion” (QIC), was proposed by Pan (2001) for GEE models. Cui (2007) developed a general computing program to implement the QIC method for a range of statistical distributions. However, only a special case of the negative binomial distribution was considered in Cui (2007), where the dispersion parameter equals to unity. This article introduces a new computing program that can be applied for the general negative binomial model, where the dispersion parameter can be any fixed value. An example is also given in this article.

Publication selection bias in minimum-wage research? A meta-regression analysis

Card and Krueger's meta-analysis of the employment effects of minimum wages challenged existing theory. Unfortunately, their meta-analysis confused publication selection with the absence of a genuine empirical effect. We apply recently developed meta-analysis methods to 64 US minimum-wage studies and corroborate that Card and Krueger's findings were nevertheless correct. The minimum-wage effects literature is contaminated by publication selection bias, which we estimate to be slightly larger than the average reported minimum-wage effect. Once this publication selection is corrected, little or no evidence of a negative association between minimum wages and employment remains.

Sex-dependent selection differentially shapes genetic variation on and off the guppy Y chromosome

Because selection is often sex-dependent, alleles can have positive effects on fitness in one sex and negative effects in the other, resulting in intralocus sexual conflict. Evolutionary theory predicts that intralocus sexual conflict can drive the evolution of sex limitation, sex-linkage, and sex chromosome differentiation. However, evidence that sex-dependent selection results in sex-linkage is limited. Here, we formally partition the contribution of Y-linked and non-Y-linked quantitative genetic variation in coloration, tail, and body size of male guppies (Poecilia reticulata)—traits previously implicated as sexually antagonistic. We show that these traits are strongly genetically correlated, both on and off the Y chromosome, but that these correlations differ in sign and magnitude between both parts of the genome. As predicted, variation in attractiveness was found to be associated with the Y-linked, rather than with the non-Y-linked component of genetic variation in male ornamentation. These findings show how the evolution of Y-linkage may be able to resolve sexual conflict. More generally, they provide unique insight into how sex-specific selection has the potential to differentially shape the genetic architecture of fitness traits across different parts of the genome.

Selection of DNA aptamers against epithelial cell adhesion molecule for cancer cell imaging and circulating tumor cell capture

Epithelial cell adhesion molecule (EpCAM) is overexpressed in most solid cancers and is an ideal antigen for clinical applications in cancer diagnosis, prognosis, imaging, and therapy. Currently, most of the EpCAM-based diagnostic, prognostic, and therapeutic strategies rely on the anti-EpCAM antibody. However, the use of EpCAM antibody is restricted due to its large size and instability. In this study, we have successfully identified DNA aptamers that selectively bind human recombinant EpCAM protein. The aptamers can specifically recognize a number of live human cancer cells derived from breast, colorectal, and gastric cancers that express EpCAM but not bind to EpCAM-negative cells. Among the aptamer sequences identified, a hairpin-structured sequence SYL3 was optimized in length, resulting in aptamer sequence SYL3C. The Kd values of the SYL3C aptamer against breast cancer cell line MDA-MB-231 and gastric cancer cell line Kato III were found to be 38±9 and 67±8 nM, respectively, which are better than that of the full-length SYL3 aptamer. Flow cytometry analysis results indicated that the SYL3C aptamer was able to recognize target cancer cells from mixed cells in cell media. When used to capture cancer cells, up to 63% cancer cell capture efficiency was achieved with about 80% purity. With the advantages of small size, easy synthesis, good stability, high binding affinity, and selectivity, the DNA aptamers reported here against cancer biomarker EpCAM will facilitate the development of novel targeted cancer therapy, cancer cell imaging, and circulating tumor cell detection.

Book-to-market ratio, default risk and return implications: from a negative perspective

A paradox is created by the common practice in stock evaluation models of excluding stocks with a negative book equity (BE). If we interpret the book-to-market ratio as a proxy for distress risk, it makes no sense to exclude these negative BE stocks since they are, prima facie, most prone to distress risk. This paper reassesses the relationship between default risk, return and the book-to-market ratio by incorporating negative BE stocks into the study. We find that negative BE stocks carry higher default risks than their positive BE counterparts and that these risks are not totally offset by higher returns. This suggests that a default risk filter can be used in the investment universe selection process through which the portfolio return can be enhanced.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.

Exploiting feature relationships towards stable feature selection

Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods.