Automated text classification of near-misses from safety reports: An improved deep learning approach

Examining past near-miss reports can provide us with information that can be used to learn about how we can mitigate and control hazards that materialise on construction sites. Yet, the process of analysing near-miss reports can be a time-consuming and labour-intensive process. However, automatic text classification using machine learning and ontology-based approaches can be used to mine reports of this nature. Such approaches tend to suffer from the problem of weak generalisation, which can adversely affect the classification performance. To address this limitation and improve classification accuracy, we develop an improved deep learning-based approach to automatically classify near-miss information contained within safety reports using Bidirectional Transformers for Language Understanding (BERT). Our proposed approach is designed to pre-train deep bi-directional representations by jointly extracting context features in all layers. We validate the effectiveness and feasibility of our approach using a database of near-miss reports derived from actual construction projects that were used to train and test our model. The results demonstrate that our approach can accurately classify ‘near misses’, and outperform prevailing state-of-the-art automatic text classification approaches. Understanding the nature of near-misses can provide site managers with the ability to identify work-areas and instances where the likelihood of an accident may occur.     

Semi-supervised machine learning approach for DDoS detection

Even though advanced Machine Learning (ML) techniques have been adopted for DDoS detection, the attack remains a major threat of the Internet. Most of the existing ML-based DDoS detection approaches are under two categories: supervised and unsupervised. Supervised ML approaches for DDoS detection rely on availability of labeled network traffic datasets. Whereas, unsupervised ML approaches detect attacks by analyzing the incoming network traffic. Both approaches are challenged by large amount of network traffic data, low detection accuracy and high false positive rates. In this paper we present an online sequential semi-supervised ML approach for DDoS detection based on network Entropy estimation, Co-clustering, Information Gain Ratio and Exra-Trees algorithm. The unsupervised part of the approach allows to reduce the irrelevant normal traffic data for DDoS detection which allows to reduce false positive rates and increase accuracy. Whereas, the supervised part allows to reduce the false positive rates of the unsupervised part and to accurately classify the DDoS traffic. Various experiments were performed to evaluate the proposed approach using three public datasets namely NSL-KDD, UNB ISCX 12 and UNSW-NB15. An accuracy of 98.23%, 99.88% and 93.71% is achieved for respectively NSL-KDD, UNB ISCX 12 and UNSW-NB15 datasets, with respectively the false positive rates 0.33%, 0.35% and 0.46%.     

SemiBoost: Boosting for Semi-Supervised Learning

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.     

Learning dataset representation for automatic machine learning algorithm selection

The algorithm selection problem is defined as identifying the best-performing machine learning (ML) algorithm for a given combination of dataset, task, and evaluation measure. The human expertise required to evaluate the increasing number of ML algorithms available has resulted in the need to automate the algorithm selection task. Various approaches have emerged to handle the automatic algorithm selection challenge, including meta-learning. Meta-learning is a popular approach that leverages accumulated experience for future learning and typically involves dataset characterization. Existing meta-learning methods often represent a dataset using predefined features and thus cannot be generalized across different ML tasks, or alternatively, learn a dataset’s representation in a supervised manner and therefore are unable to deal with unsupervised tasks. In this study, we propose a novel learning-based task-agnostic method for producing dataset representations. Then, we introduce TRIO, a meta-learning approach, that utilizes the proposed dataset representations to accurately recommend top-performing algorithms for previously unseen datasets. TRIO first learns graphical representations for the datasets, using four tools to learn the latent interactions among dataset instances and then utilizes a graph convolutional neural network technique to extract embedding representations from the graphs obtained. We extensively evaluate the effectiveness of our approach on 337 datasets and 195 ML algorithms, demonstrating that TRIO significantly outperforms state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.

     

Semi-supervised model-based document clustering: A comparative study

Semi-supervised learning has become an attractive methodology for improving classification models and is often viewed as using unlabeled data to aid supervised learning. However, it can also be viewed as using labeled data to help clustering, namely, semi-supervised clustering. Viewing semi-supervised learning from a clustering angle is useful in practical situations when the set of labels available in labeled data are not complete, i.e., unlabeled data contain new classes that are not present in labeled data. This paper analyzes several multinomial model-based semi-supervised document clustering methods under a principled model-based clustering framework. The framework naturally leads to a deterministic annealing extension of existing semi-supervised clustering approaches. We compare three (slightly) different semi-supervised approaches for clustering documents: Seeded damnl, Constrained damnl, and Feedback-based damnl, where damnl stands for multinomial model-based deterministic annealing algorithm. The first two are extensions of the seeded k-means and constrained k-means algorithms studied by Basu et al. (2002); the last one is motivated by Cohn et al. (2003). Through empirical experiments on text datasets, we show that: (a) deterministic annealing can often significantly improve the performance of semi-supervised clustering; (b) the constrained approach is the best when available labels are complete whereas the feedback-based approach excels when available labels are incomplete. Editor: Andrew Moore     

Semi-supervised classification trees

In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and unlabeled data, while preserving all of the appealing characteristics of standard supervised decision trees: being non-parametric, efficient, having good predictive performance and producing readily interpretable models. Moreover, we further improve their predictive performance by using them as base predictive models in random forests. We performed an extensive empirical evaluation on 12 binary and 12 multi-class classification datasets. The results showed that the proposed methods improve the predictive performance of their supervised counterparts. Moreover, we show that, in cases with limited availability of labeled data, the semi-supervised decision trees often yield models that are smaller and easier to interpret than supervised decision trees.     

A fast quasi-Newton method for semi-supervised SVM

Due to its wide applicability, semi-supervised learning is an attractive method for using unlabeled data in classification. In this work, we present a semi-supervised support vector classifier that is designed using quasi-Newton method for nonsmooth convex functions. The proposed algorithm is suitable in dealing with very large number of examples and features. Numerical experiments on various benchmark datasets showed that the proposed algorithm is fast and gives improved generalization performance over the existing methods. Further, a non-linear semi-supervised SVM has been proposed based on a multiple label switching scheme. This non-linear semi-supervised SVM is found to converge faster and it is found to improve generalization performance on several benchmark datasets.     

基于混合样本自动数据增强技术的半监督学习方法

基于一致性的半监督学习方法通常使用简单的数据增强方法来实现对原始输入和扰动输入的一致性预测.在有标签数据的比例较低的情况下,该方法的效果难以得到保证.将监督学习中一些先进的数据增强方法扩展到半监督学习环境中,是解决该问题的思路之一.基于一致性的半监督学习方法MixMatch,提出了基于混合样本自动数据增强技术的半监督学...     

A non-parametric semi-supervised discretization method

Semi-supervised classification methods aim to exploit labeled and unlabeled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method, which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. Then, an in-depth comparison of this semi-supervised method with the original supervised MODL approach is presented. We demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.     

Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio

Sentiment analysis is an active research area in today’s era due to the abundance of opinionated data present on online social networks. Semantic detection is a sub-category of sentiment analysis which deals with the identification of sentiment orientation in any text. Many sentiment applications rely on lexicons to supply features to a model. Various machine learning algorithms and sentiment lexicons have been proposed in research in order to improve sentiment categorization. Supervised machine learning algorithms and domain specific sentiment lexicons generally perform better as compared to the unsupervised or semi-supervised domain independent lexicon based approaches. The core hindrance in the application of supervised algorithms or domain specific sentiment lexicons is the unavailability of sentiment labeled training datasets for every domain. On the other hand, the performance of algorithms based on general purpose sentiment lexicons needs improvement. This research is focused on building a general purpose sentiment lexicon in a semi-supervised manner. The proposed lexicon defines word semantics based on Expected Likelihood Estimate Smoothed Odds Ratio that are then incorporated with supervised machine learning based model selection approach. A comprehensive performance comparison verifies the superiority of our proposed approach.     

Automatic feature selection for supervised learning in link prediction applications: a comparative study

For the last years, a considerable amount of attention has been devoted to the research about the link prediction (LP) problem in complex networks. This problem tries to predict the likelihood of an association between two not interconnected nodes in a network to appear in the future. One of the most important approaches to the LP problem is based on supervised machine learning (ML) techniques for classification. Although many works have presented promising results with this approach, choosing the set of features (variables) to train the classifiers is still a major challenge. In this article, we report on the effects of three different automatic variable selection strategies (Forward, Backward and Evolutionary) applied to the feature-based supervised learning approach in LP applications. The results of the experiments show that the use of these strategies does lead to better classification models than classifiers built with the complete set of variables. Such experiments were performed over three datasets (Microsoft Academic Network, Amazon and Flickr) that contained more than twenty different features each, including topological and domain-specific ones. We also describe the specification and implementation of the process used to support the experiments. It combines the use of the feature selection strategies, six different classification algorithms (SVM, K-NN, naïve Bayes, CART, random forest and multilayer perceptron) and three evaluation metrics (Precision, F-Measure and Area Under the Curve). Moreover, this process includes a novel ML voting committee inspired approach that suggests sets of features to represent data in LP applications. It mines the log of the experiments in order to identify sets of features frequently selected to produce classification models with high performance. The experiments showed interesting correlations between frequently selected features and datasets.     

Manifold proximal support vector machine for semi-supervised classification

Recently, semi-supervised learning (SSL) has attracted a great deal of attention in the machine learning community. Under SSL, large amounts of unlabeled data are used to assist the learning procedure to construct a more reasonable classifier. In this paper, we propose a novel manifold proximal support vector machine (MPSVM) for semi-supervised classification. By introducing discriminant information in the manifold regularization (MR), MPSVM not only introduces MR terms to capture as much geometric information as possible from inside the data, but also utilizes the maximum distance criterion to characterize the discrepancy between different classes, leading to the solution of a pair of eigenvalue problems. In addition, an efficient particle swarm optimization (PSO)-based model selection approach is suggested for MPSVM. Experimental results on several artificial as well as real-world datasets demonstrate that MPSVM obtains significantly better performance than supervised GEPSVM, and achieves comparable or better performance than LapSVM and LapTSVM, with better learning efficiency.     

结合支持向量机与半监督K-means的新型学习算法

半监督学习结合少量有标签样本和大量无标签样本,可以有效提高算法的泛化性能。传统的半监督支持向量机（SVM）算法在目标函数中引入无标签样本的依赖项来推动决策面通过低密度区域,但往往会带来高计算复杂度和局部最优解等问题。同时,半监督K-means算法面临着如何有效利用监督信息进行质心的初始化及更新等问题。针对上述问题,提出了一种结合SVM和半监督K-means的新型学习算法（SKAS）。首先,提出一种改进的半监督K-means算法,从距离度量和质心迭代两个方面进行了改进;然后,设计了一种融合算法将半监督K-means算法与SVM相结合以进一步提升算法性能。在6个UCI数据集上的实验结果表明,所提算法在其中5个数据集上的运行结果都优于当前先进的半监督SVM算法和半监督K-means算法,且拥有最高的平均准确率。     

Constraint Score Evaluation for Spectral Feature Selection

Semi-supervised context characterized by the presence of a few pairs of constraints between learning samples is abundant in many real applications. Analysing these instance constraints by recent spectral scores has shown good performances for semi-supervised feature selection. The performance evaluation of these scores is generally based on classification accuracy and is performed in a ground truth context. However, this supervised context used by the evaluation step is inconsistent with the semi-supervised context in which the feature selection operates. In this paper, we propose a semi-supervised performance evaluation procedure, so that both feature selection and clustering steps take into account the constraints given by the user. In this way, the selection and the evaluation steps are performed in the same context which is close to real life applications. Extensive experiments on benchmark datasets are carried out in the last section. These experiments are performed using a supervised classical evaluation and the semi-supervised proposed one. They demonstrate the effectiveness of feature selection based on constraint analysis that uses both pairwise constraints and the information brought by the unlabeled data.     

Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning

This paper presents the modelling possibilities of kernel-based approaches to a complex real-world problem, i.e. corporate and municipal credit rating classification. Based on a model design that includes data pre-processing, the labelling of individual parameter vectors using expert knowledge, the design of various support vector machines with supervised learning as well as kernel-based approaches with semi-supervised learning, this modelling is undertaken in order to classify objects into rating classes. The results show that the rating classes assigned to bond issuers can be classified with high classification accuracy using a limited subset of input variables. This holds true for kernel-based approaches with both supervised and semi-supervised learning.     

基于代价极速学习机的软件缺陷报告分类方法

在所有的软件系统开发过程中,Bug的存在是不可避免的问题.对于软件系统的开发者来说,修复Bug最有利的工具就是Bug报告.但是人工识别Bug报告会给开发人员带来新的负担,因此,自动对Bug报告进行分类是一项很有必要的工作.基于此,提出用基于极速学习机的方法来对Bug报告进行分类.具体而言,主要解决Bug报告自动分类的3个问题：第1个是Bug报告数据集里不同类别的样本数量不平衡问题;第2个是Bug报告数据集里被标注的样本不充足问题;第3个是Bug报告数据集总体样本量不充足问题.为了解决这3个问题,分别引入了基于代价的有监督分类方法、基于模糊度的半监督学习方法以及样本迁移方法.通过在多个Bug报告数据集上进行实验,验证了这些方法的可行性和有效性.     

基于集成学习的半监督情感分类方法研究

情感分类旨在对文本所表达的情感色彩类别进行分类的任务。该文研究基于半监督学习的情感分类方法,即在很少规模的标注样本的基础上,借助非标注样本提高情感分类性能。为了提高半监督学习能力,该文提出了一种基于一致性标签的集成方法,用于融合两种主流的半监督情感分类方法:基于随机特征子空间的协同训练方法和标签传播方法。首先,使用这两种半监督学习方法训练出的分类器对未标注样本进行标注;其次,选取出标注一致的未标注样本;最后,使用这些挑选出的样本更新训练模型。实验结果表明,该方法能够有效降低对未标注样本的误标注率,从而获得比任一种半监督学习方法更好的分类效果。     

A hybrid classification scheme for mining multisource geospatial data

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of the large number of accurate training samples (10 to 30 × |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of the statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately, there is no convenient multivariate statistical model that can be employed for multisource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on Landsat satellite image datasets, and our new hybrid approach shows over 24% to 36% improvement in overall classification accuracy over conventional classification schemes.     

Identification of non-functional requirements in textual specifications: A semi-supervised learning approach

ContextEarly detection of non-functional requirements (NFRs) is crucial in the evaluation of architectural alternatives starting from initial design decisions. The application of supervised text categorization strategies for requirements expressed in natural language has been proposed in several works as a method to help analysts in the detection and classification of NFRs concerning different aspects of software. However, a significant number of pre-categorized requirements are needed to train supervised text classifiers, which implies that analysts have to manually assign categories to numerous requirements before being able of accurately classifying the remaining ones.ObjectiveWe propose a semi-supervised text categorization approach for the automatic identification and classification of non-functional requirements. Therefore, a small number of requirements, possibly identified by the requirement team during the elicitation process, enable learning an initial classifier for NFRs, which could successively identify the type of further requirements in an iterative process. The goal of the approach is the integration into a recommender system to assist requirement analysts and software designers in the architectural design process.MethodDetection and classification of NFRs is performed using semi-supervised learning techniques. Classification is based on a reduced number of categorized requirements by taking advantage of the knowledge provided by uncategorized ones, as well as certain properties of text. The learning method also exploits feedback from users to enhance classification performance.ResultsThe semi-supervised approach resulted in accuracy rates above 70%, considerably higher than the results obtained with supervised methods using standard collections of documents.ConclusionEmpirical evidence showed that semi-supervision requires less human effort in labeling requirements than fully supervised methods, and can be further improved based on feedback provided by analysts. Our approach outperforms previous supervised classification proposals and can be further enhanced by exploiting feedback provided by analysts.     

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context.