Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16 to 24 data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost.
Different from automation of model selection and tuning where the literature is very rich, only a few works have been proposed to automate feature engineering. The main reason is that feature engineering is both domain and data specific. In fact, it requires a lot of data exploration with deep domain knowledge to search for relevant patterns in the data. However, recent work shows that, for a specific type of problem and data such as provided in relational databases, automation of feature engineering is achievable @cite_10 .
@cite_10
2038464048 2099102906 2066477694 2108599331 2889153461 2136051823 2096232175 2033088400 2964182926 2037931255 2890336707 1952835952 49111948 2167101736 2888851832 2260771218 2165310684 2766946944 2615577556 2953127297
The data mining and machine learning community is often faced with two key problems: working with imbalanced data and selecting the best features for machine learning. This paper presents a process involving a feature selection technique for selecting the important attributes and a data sampling technique for addressing class imbalance. The application domain of this study is software engineering, more specifically, software quality prediction using classification models. When using feature selection and data sampling together, different scenarios should be considered. The four possible scenarios are: (1) feature selection based on original data, and modeling (defect prediction) based on original data; (2) feature selection based on original data, and modeling based on sampled data; (3) feature selection based on sampled data, and modeling based on original data; and (4) feature selection based on sampled data, and modeling based on sampled data. The research objective is to compare the software defect prediction performances of models based on the four scenarios. The case study consists of nine software measurement data sets obtained from the PROMISE software project repository. Empirical results suggest that feature selection based on sampled data performs significantly better than feature selection based on original data, and that defect prediction models perform similarly regardless of whether the training data was formed using sampled or original data. There is an arms race in the data management industry to support analytics, in which one critical step is feature selection, the process of selecting a feature set that will be used to build a statistical model. Analytics is one of the biggest topics in data management, and feature selection is widely regarded as the most critical step of analytics; thus, we argue that managing the feature selection process is a pressing data management challenge. We study this challenge by describing a feature-selection language and a supporting prototype system that builds on top of current industrial, R-integration layers. From our interactions with analysts, we learned that feature selection is an interactive, human-in-the-loop process, which means that feature selection workloads are rife with reuse opportunities. Thus, we study how to materialize portions of this computation using not only classical database materialization optimizations but also methods that have not previously been used in database optimization, including structural decomposition methods (like QR factorization) and warmstart. These new methods have no analog in traditional SQL systems, but they may be interesting for array and scientific database applications. On a diverse set of data sets and programs, we find that traditional database-style approaches that ignore these new opportunities are more than two orders of magnitude slower than an optimal plan in this new tradeoff space across multiple R-backends. Furthermore, we show that it is possible to build a simple cost-based optimizer to automatically select a near-optimal execution plan for feature selection. Much research energy in software engineering is focused on the creation of effort and defect prediction models. Such models are important means for practitioners to judge their current project situation, optimize the allocation of their resources, and make informed future decisions. However, software engineering data contains a large amount of variability. Recent research demonstrates that such variability leads to poor fits of machine learning models to the underlying data, and suggests splitting datasets into more fine-grained subsets with similar properties. In this paper, we present a comparison of three different approaches for creating statistical regression models to model and predict software defects and development effort. Global models are trained on the whole dataset. In contrast, local models are trained on subsets of the dataset. Last, we build a global model that takes into account local characteristics of the data. We evaluate the performance of these three approaches in a case study on two defect and two effort datasets. We find that for both types of data, local models show a significantly increased fit to the data compared to global models. The substantial improvements in both relative and absolute prediction errors demonstrate that this increased goodness of fit is valuable in practice. Finally, our experiments suggest that trends obtained from global models are too general for practical recommendations. At the same time, local models provide a multitude of trends which are only valid for specific subsets of the data. Instead, we advocate the use of trends obtained from global models that take into account local characteristics, as they combine the best of both worlds. Discriminative methods for learning structured models have enabled wide-spread use of very rich feature representations. However, the computational cost of feature extraction is prohibitive for large-scale or time-sensitive applications, often dominating the cost of inference in the models. Significant efforts have been devoted to sparsity-based model selection to decrease this cost. Such feature selection methods control computation statically and miss the opportunity to fine-tune feature extraction to each input at run-time. We address the key challenge of learning to control fine-grained feature extraction adaptively, exploiting non-homogeneity of the data. We propose an architecture that uses a rich feedback loop between extraction and prediction. The run-time control policy is learned using efficient value-function approximation, which adaptively determines the value of information of features at the level of individual variables for each input. We demonstrate significant speedups over state-of-the-art methods on two challenging datasets. For articulated pose estimation in video, we achieve a more accurate state-of-the-art model that is also faster, with similar results on an OCR task. Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift. Target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based discriminative feature learning method and a center-based discriminative feature learning method are proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain adaptation methods. Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes features. To address this limitation, we investigate the problem of online feature selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features. The key challenge of online feature selection is how to make accurate prediction for an instance using a small number of active features. This is in contrast to the classical setup of online learning where all the features can be used for prediction. We attempt to tackle this challenge by studying sparsity regularization and truncation techniques. Specifically, this article addresses two different tasks of online feature selection: 1) learning with full input, where an learner is allowed to access all the features to decide the subset of active features, and 2) learning with partial input, where only a limited number of features is allowed to be accessed for each instance by the learner. We present novel algorithms to solve each of the two problems and give their performance analysis. We evaluate the performance of the proposed algorithms for online feature selection on several public data sets, and demonstrate their applications to real-world problems including image classification in computer vision and microarray gene expression analysis in bioinformatics. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques. Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available. This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine an appropriate set of inputs for the networks it evolves. By learning the network's inputs, topology, and weights simultaneously, FS-NEAT addresses the feature selection problem without relying on meta-learning or labeled data. Initial experiments in an autonomous car racing simulation demonstrate that FS-NEAT can learn better and faster than regular NEAT. In addition, the networks it evolves are smaller and require fewer inputs. Furthermore, FS-NEAT's performance remains robust even as the feature selection task it faces is made increasingly difficult. The rapidly evolving landscape of multicore architectures makes the construction of efficient libraries a daunting task. A family of methods known collectively as “auto-tuning” has emerged to address this challenge. Two major approaches to auto-tuning are empirical and model-based: empirical autotuning is a generic but slow approach that works by measuring runtimes of candidate implementations, model-based auto-tuning predicts those runtimes using simplified abstractions designed by hand. We show that machine learning methods for non-linear regression can be used to estimate timing models from data, capturing the best of both approaches. A statistically-derived model offers the speed of a model-based approach, with the generality and simplicity of empirical auto-tuning. We validate our approach using the filterbank correlation kernel described in Pinto and Cox [2012], where we find that 0.1 seconds of hill climbing on the regression model (“predictive auto-tuning”) can achieve almost the same speed-up as is brought by minutes of empirical auto-tuning. Our approach is not specific to filterbank correlation, nor even to GPU kernel auto-tuning, and can be applied to almost any templated-code optimization problem, spanning a wide variety of problem types, kernel types, and platforms. Feature engineering has been the key to the success of many prediction models. However, the process is nontrivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage. The rapid advance in information processing systems in recent decades had directed engineering research towards the development of intelligent systems that can evolve models of natural phenomena automatically—“by themselves,” so to speak. In this respect, a wide range of machine learning techniques like decision trees, artificial neural networks (ANNs), Bayesian methods, fuzzy-rule based systems, and evolutionary algorithms have been successfully applied to model different civil engineering systems. In this study, the possibility of using yet another machine learning paradigm that is firmly based on the theory of statistical learning, namely that of the support vector machine (SVM), is investigated. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this paper, the basic ... Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a target domain whose distribution differs from the training data distribution, referred as the source domain. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples. The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and observe that it provides significant performance improvements on several domain adaptation benchmarks. Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes) implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems. thousands, are becoming common. Therefore, feature selection has been an active research area in pattern recognition, statistics and data mining communities. The main idea of feature reduction is to select a subset of input variables by eliminating features with little or no predictive ability, but without scarifying the performance of the model built on the chosen features. It is also known as variable selection, feature reduction, attribute selection or variable subset selection. By removing most of the irrelevant and redundant features from the data, feature reduction brings many potential benefits to us:  Alleviating the effect of the curse of dimensionality to improve prediction performance;  Facilitating data visualization and data Plenty of feature selection methods are available in literature due to the availability of data with hundreds of variables leading to data with very high dimension. Feature selection methods provides us a way of reducing computation time, improving prediction performance, and a better understanding of the data in machine learning or pattern recognition applications. In this paper we provide an overview of some of the methods present in literature. The objective is to provide a generic introduction to variable elimination which can be applied to a wide array of machine learning problems. We focus on Filter, Wrapper and Embedded methods. We also apply some of the feature selection techniques on standard datasets to demonstrate the applicability of feature selection techniques. In most domain adaption approaches, all features are used for domain adaption. However, often, not every feature is beneficial for domain adaption. In such cases, incorrectly involving all features might cause the performance to degrade. In other words, to make the model trained on the source domain work well on the target domain, it is desirable to find invariant features for domain adaption rather than using all features. However, invariant features across domains may lie in a higher order space, instead of in the original feature space. Moreover, the discriminative ability of some invariant features such as shared background information is weak, and needs to be further filtered. Therefore, in this paper, we propose a novel domain adaption algorithm based on an explicit feature map and feature selection. The data are first represented by a kernel-induced explicit feature map, such that high-order invariant features can be revealed. Then, by minimizing the marginal distribution difference, conditional distribution difference, and the model error, the invariant discriminative features are effectively selected. This problem is NP-hard to be solved, and we propose to relax it and solve it by a cutting plane algorithm. Experimental results on six real-world benchmarks have demonstrated the effectiveness and efficiency of the proposed algorithm, which outperforms many state-of-the-art domain adaption approaches. Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity-based, information-theoretical-based, sparse-learning-based, and statistical-based methods. To facilitate and promote the research in this community, we also present an open source feature selection repository that consists of most of the popular feature selection algorithms (http: featureselection.asu.edu ). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research. While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, which is usually challenging for online learning from a massive stream of data. In this paper, we propose an incremental feature learning algorithm to determine the optimal model complexity for large-scale, online datasets based on the denoising autoencoder. This algorithm is composed of two processes: adding features and merging features. Specifically, it adds new features to minimize the objective function’s residual and merges similar features to obtain a compact feature representation and prevent over-fitting. Our experiments show that the proposed model quickly converges to the optimal number of features in a large-scale online setting. In classification tasks, our model outperforms the (non-incremental) denoising autoencoder, and deep networks constructed from our algorithm perform favorably compared to deep belief networks and stacked denoising autoencoders. Further, the algorithm is eective in recognizing new patterns when the data distribution changes over time in the massive online data stream. Engineering phase specific tailored views on data are required to prevent over-engineering and to discover missing elements. This paper presents an approach to enhance a semantic Conceptual Data Model (CDM) to define the specific behavior of engineering phases. Therefore, a CDM gets enhanced by an ontology profile to specify required and non-admissible features for each engineering phase. This is exploited to enhance exis­ting transformations from Web Ontology Language (OWL) to Eclipse Modeling Language (EMF) to generate phase specific decorators representing the corresponding views on data items. The approach also supports engineering phase specific handling of constraints and rules that further augment the guidance of engineers through the system development process by applying knowledge management functions such as reasoning. As a result, the presented approach reveals a notably higher data quality and offers more analysis potential by enabling phase-specific definitions of views on data and specification of semantic checks on CDM level. Several use cases from aerospace engineering have been analyzed and the improvements in areas such as inconsistency detection, knowledge derivation, and guided system modeling are highlighted in this paper. Model-based Systems Engineering requires an intuitive semantically strong data model to enable precise data specification and provide the foundation for fruitful data analyses during data evolution. This paper presents an approach to use the Web Ontology Language (OWL) for specifying a Conceptual Data Model (CDM) being transformed into a format understandable by the Eclipse Modeling Framework (EMF) to profit from powerful data handling and knowledge management functions during runtime. Coalescing OWL with EMF brings up the strength of both approaches leading to considerably better data models with less failure potential and reveal notably more analysis potential by using a common data model specification. This approach also enables the direct application of reasoning functionality for automatic inference of several pieces of knowledge and automatic checks as illustrated by examples from aerospace industry. The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic to real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Existing approaches focus either on mapping representations from one domain to the other, or on learning to extract features that are invariant to the domain from which they were extracted. However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. We suggest that explicitly modeling what is unique to each domain can improve a model's ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains. Our model is trained not only to perform the task we care about in the source domain, but also to use the partitioned representation to reconstruct the images from both domains. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.