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In real-world machine learning applications, there is a cost associated with sampling of different features. Budgeted learning can be used to select which feature-values to acquire from each instance in a dataset, such that the best model is induced under a given constraint. However, this approach is not possible in the domain of online learning since one may not retroactively acquire feature-values from past instances. In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream. In this paper we introduce the issue of online budgeted learning and describe a general framework for addressing this challenge. We propose two types of feature value acquisition policies based on the multi-armed bandit problem: random and adaptive. Adaptive policies perform online adjustments according to new information coming from a data stream, while random policies are not sensitive to the information that arrives from the data stream. Our comparative study on five real-world datasets indicates that adaptive policies outperform random policies for most budget limitations and datasets. Furthermore, we found that in some cases adaptive policies achieve near-optimal results.
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Introduction to Data Mining Decision Trees Clustering Techniques Ensemble Methods Decomposition Methodology in Data Mining Feature Set Decomposition Space Decomposition Sample Decomposition Function Decomposition Concept Decomposition Automatic Decomposition Conclusions, Advanced Issues and Open Questions.
Active learning is the process in which unlabeled in- stances are dynamically selected for expert labelling, and then a clas- sifier is trained on the labeled data. Active learning is particularly useful when there is a large set of unlabeled instances, and acquir- ing a label is costly. In business scenarios such as direct marketing, active learning can be used to indicate which customer to approach such that the potential benefit from the approached customer can cover the cost of approach. This paper presents a new algorithm for cost-sensitive active learning using a conditional expectation es- timator. The new estimator focuses on acquisitions that are likely to improve the profit. Moreover, we investigate simulated annealing techniques to combine exploration with exploitation in the classi- fier construction. Using five evaluation metrics, we evaluated the algorithm on four benchmark datasets. The results demonstrate the superiority of the proposed method compared to other algorithms.
In many modern manufacturing plants, data that characterize the manufacturing process are electronically collected and stored in the organization's databases. Thus, data mining tools can be used for automatically discovering interesting and useful patterns in the manufacturing processes. These patterns can be subsequently exploited to enhance the whole manufacturing process in such areas as defect prevention and detection, reducing flow-time, increasing safety, etc. When data mining is directed towards improving manufacturing process, there are certain distinctions that should be noted compared to the classical methods employed in quality engineering, such as the experimental design. In data mining the primary purpose of the targeted database is not data analysis; the volume of the collected data makes it impractical to explore it using standard statistical procedures (Braha and Shmilovici, 2003). Request access from your librarian to read this chapter's full text.
Adverse drug interactions are largely preventable causes of medical accidents, which frequently result in physician and emergency room encounters. The detection of drug interactions in a lab, prior to a drug's use in medical practice, is essential, however it is costly and time-consuming. Machine learning techniques can provide an efficient and accurate means of predicting possible drug-drug interactions and combat the growing problem of adverse drug interactions. Most existing models for predicting interactions rely on the chemical properties of drugs. While such models can be accurate, the required properties are not always available.
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This chapter presents three case studies in the data leakage domain and the methods proposed and evaluated for mitigating the threat of data leakage. The case studies are: detecting an insider attempting to misuse and leak data stored in a database system; using honeytokens to detect insider threats; and detecting leakage through email.
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Deep Neural Networks (DNNs) have become effective for various machine learning tasks. DNNs are known to achieve high accuracy with unstructured data in which each data sample (e.g., image) consists of many raw features (e.g., pixels) of the same type. The effectiveness of this approach diminishes for structured (tabular) data. In most cases, decision tree-based models such as Random Forest (RF) or Gradient Boosting Decision Trees (GBDT) outperform DNNs. In addition, DNNs tend to perform poorly when the number of samples in the dataset is small. This paper introduces Transfer Learning for Tabular Data (TLTD) which utilizes a novel learning architecture designed to extract new features from structured datasets. Using the DNN’s learning capabilities on images, we convert the tabular data into images, then use the distillation technique to achieve better learning. We evaluated our approach with 25 structured datasets, and compared the outcomes to those of RF, eXtreme Gradient Boosting (XGBoost), Tabnet, KNN, and TabPFN. The results demonstrate the usefulness of the TLTD approach.
Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared with the RAKEL algorithm and to other state-of-the-art algorithms.
