Toward economic machine learning and utility-based data mining

Early work in predictive data mining and machine learning rarely addressed the complex circumstances in which knowledge is extracted and applied. It often assumed that training data were freely available and focused on simple objectives, namely predictive accuracy. Over time, there has been a growing interest in the machine learning and data mining communities in research addressing economical data acquisition, utility-based methods for knowledge induction and application and methodologies for evaluating the utility derived from data mining techniques.This workshop explores the notion of economic utility and how it can be maximized throughout the data mining process. As of today much of the work focuses on a single aspect data mining. The workshop aims to bring together researchers from data mining and machine learning to share their perspective on key challenges in utility-based data mining and how individual contributions made thus far can be combined towards a comprehensive utility-based methodology.We believe the very positive response we have had from both academia and industry indicates the importance of utility-based data mining research and hope that the workshop will promote a fruitful exchange of ideas to further advance the field.

It is often expensive to acquire data in real-world data mining applications. Most previous data mining and machine learning research, however, assumes that a fixed set of training examples is given. In this paper, we propose an online cost-sensitive framework that allows a learner to dynamically acquire examples as it learns, and to decide the ideal number of examples needed to minimize the total cost. We also propose a new strategy for Partial Example Acquisition (PAS), in which the learner can acquire examples with a subset of attribute values to reduce the data acquisition cost. Experiments on UCI datasets show that the new PAS strategy is an effective method in reducing the total cost for data acquisition.

Semi-supervised learning has shown its potential in many real-world applications where only few labeled examples are available. However, when some fairness constraints need to be satisfied, semi-supervised classification models often struggle as they are required to cope with the lack of sufficient information for predicting the target variable while forgetting its relationships with any sensitive and potentially discriminatory attribute. To address this issue, we propose a fair semi-supervised representation learning architecture that leads to fair and accurate classification results even in very challenging scenarios with few labeled (but biased) instances. We show experimentally that our model can be easily adopted in very general settings, as the learned representations may be employed to train any supervised classifier. Moreover, when applied to several synthetic and real-world datasets, our method is competitive with state-of-the-art fair semi-supervised approaches.

Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories

Recently the field of machine learning, pattern recognition, and data mining has witnessed a new research stream that is <i>learning with partial supervisio</i>n -LPS- (known also as <i>semi-supervised learning</i>). This learning scheme is motivated by the fact that the process of acquiring the labeling information of data could be quite costly and sometimes prone to mislabeling. The general spectrum of learning from data is envisioned in Figure 1. As shown, in many situations, the data is neither perfectly nor completely labeled.<div><br></div><div>LPS aims at using available labeled samples in order to guide the process of building classification and clustering machineries and help boost their accuracy. Basically, LPS is a combination of two learning paradigms: supervised and unsupervised where the former deals exclusively with labeled data and the latter is concerned with unlabeled data. Hence, the following questions:</div><div><br></div><div><ul><li>Can we improve supervised learning with unlabeled data? <br></li><li>Can we guide unsupervised learning by incorporating few labeled samples?<br></li></ul></div><div><br></div><div>Typical LPS applications are medical diagnosis (Bouchachia & Pedrycz, 2006a), facial expression recognition (Cohen et al., 2004), text classification (Nigam et al., 2000), protein classification (Weston et al., 2003), and several natural language processing applications such as word sense disambiguation (Niu et al., 2005), and text chunking (Ando & Zhangz, 2005).</div><div><br></div><div>Because LPS is still a young but active research field, it lacks a survey outlining the existing approaches and research trends. In this chapter, we will take a step towards an overview. We will discuss (i) the background of LPS, (iii) the main focus of our LPS research and explain the underlying assumptions behind LPS, and (iv) future directions and challenges of LPS research. </div>

Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine–machine framework when compared with traditional machine–human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible–Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the context of hyperspectral image classification.

In recent years, machine learning has been vastly used in many scientific experiments to analyze and predict the data [1]. Since there are many types of machine learning and each of them has additional further branches, it's necessary to understand which one can predict or summarize the data with more accuracy. This paper mainly focuses on traditional machine learning and active learning. To compare the effects of these two types of machine learning, the paper uses a dataset about using metal oxide semiconductor (MOX) to measure the concentration of carbon monoxide (CO). In the first part, the paper tests the accuracy of predicting CO concentration with 3 types of traditional machine learning: Decision Tree, Random Forest and K-nearest neighbors. In the second part, the paper chooses the traditional machine learning which has the best performance in the first part and compares the accuracy of it with the active learning on predicting CO concentration. The result is that the classification accuracy of Random Forest is 0.625, which is the highest among them. And active learning is generally better than traditional machine learning when training samples are small, and they have a similar accuracy when the training sample is enough.

Assessing the quality of discovered results is an important open problem in data mining. Such assessment is particularly vital when mining itemsets, since commonly many of the discovered patterns can be easily explained by background knowledge. The simplest approach to screen uninteresting patterns is to compare the observed frequency against the independence model. Since the parameters for the independence model are the column margins, we can view such screening as a way of using the column margins as background knowledge. In this paper we study techniques for more flexible approaches for infusing background knowledge. Namely, we show that we can efficiently use additional knowledge such as row margins, lazarus counts, and bounds of ones. We demonstrate that these statistics describe forms of data that occur in practice and have been studied in data mining. To infuse the information efficiently we use a maximum entropy approach. In its general setting, solving a maximum entropy model is infeasible, but we demonstrate that for our setting it can be solved in polynomial time. Experiments show that more sophisticated models fit the data better and that using more information improves the frequency prediction of itemsets.

Matrix factorization is one of the most popular techniques for prediction problems in the fields of intelligent systems and data mining. It has shown its effectiveness in many real-world applications such as recommender systems. As a collaborative filtering method, it gives users recommendations based on their previous preferences or ratings. Due to the extreme sparseness of the ratings matrix, active learning is used for eliciting ratings for a user to get better recommendations. In this paper, we propose a new matrix factorization model called Enhanced SVD ESVD which combines the classic matrix factorization method with a specific rating elicitation strategy. We evaluate the proposed ESVD method on the Movielens data set, and the experimental results suggest its effectiveness in terms of both accuracy and efficiency, when compared with traditional matrix factorization methods and active learning methods.

Data mining remains an important research area in database systems. We present a review of processing alternatives, storage mechanisms, algorithms, data structures and optimizations that enable data mining on large data sets. We focus on the computation of well-known multidimensional statistical and machine learning models. We pay particular attention to SQL and MapReduce as two competing technologies for large scale processing. We conclude with a summary of solved major problems and open research issues.

The primary role of intelligence organisations and public safety agencies encompasses protecting the lives and property of citizens. However, the urban population growth rate tends to overshadow the available security resources. Thus, the security agencies appear to be more reactive than proactive. Public safety agencies usually have a plethora of under-utilised crime incident reports at their disposal, which if efficiently analysed could reveal some previously unknown useful information. Such information reveals insights into a range of functions in a crime investigation, which can assist in determining criminal trends and in knowledge-driven decision support. This chapter provides an overview of data mining techniques and existing applications used in this domain of interest. Features of existing applications and techniques, such as exploratory basis, model selection, algorithm advancement and result summary, are compared. Future potential of crime data mining, and open research issues, are also discussed.

Imbalanced data classification is one of the challenging problems in data mining and machine learning research. The traditional classification algorithms are often biased towards the majority class when learning from imbalanced data. Much work have been proposed to address this problem, including data re-sampling, algorithm modification, and cost-sensitive learning. However, most of them focus on one of these techniques. This paper proposes to utilize both algorithm modification and cost-sensitive learning based on decision-theoretic rough set (DTRS) model. In particular, we use naive Bayes classifier as the base classifier and modify it for imbalanced learning. For cost-sensitive learning, we adopt the systematic method from DTRS to derive required thresholds that have the minimum decision cost. Our experimental results on three well-known text classification databases show that unified DTRS provides similar performance on balanced class distribution, outperforms naive Bayes classifier on imbalanced datasets, and is competitive with other imbalanced learning classifier.

Deep learning technologies have been successfully applied to hyperspectral (HS) image classification with remarkable performance. However, compared with traditional machine learning methods, neural networks usually need more data. In remote sensing (RS) research, obtaining a large number of labeled HS data is very difficult and expensive work. Simultaneously, the distribution of feature information is bound to be unbalanced, and tends to conform to the long tail. At present, the neighborhood information of unlabeled samples is usually ignored in HS image classification tasks based on semi-supervised learning. In this letter, we propose a new semi-supervised long-tail learning framework based on spatial neighborhood information (SLN-SNI), which can complete the HS image classification task under unbalanced small sample data. Specifically, a new semi-supervised learning strategy is proposed. On this basis, a new method to determine the label of unlabeled samples based on spatial neighborhood information (SNI) is proposed. The coarse classification results divided into three situations are judged again, and the accuracy of pseudo labels is improved. The performance of the proposed method is tested on three public HS image datasets. Compared with the current advanced methods have achieved a certain improvement.

Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

Semi-supervised matrixized least squares support vector machine