Underlying Learning Algorithms Research Articles

MFILS: Tri-Selection via Convex and Nonconvex Regularizations.

In many real-world applications, data are represented by multiple instances and simultaneously associated with multiple labels. These data are always redundant and generally contaminated by different noise levels. As a result, several machine learning models fail to achieve good classification and find an optimal mapping. Feature selection, instance selection, and label selection are three effective dimensionality reduction techniques. Nevertheless, the literature was limited to feature and/or instance selection but has, to some extent, neglected label selection, which also plays an essential role in the preprocessing step, as label noises can adversely affect the performance of the underlying learning algorithms. In this article, we propose a novel framework termed multilabel Feature Instance Label Selection (mFILS) that simultaneously performs feature, instance, and label selections in both convex and nonconvex scenarios. To the best of our knowledge, this article offers, for the first time ever, a study using the triple and simultaneous selection of features, instances, and labels based on convex and nonconvex penalties in a multilabel scenario. Experimental results are built on some known benchmark datasets to validate the effectiveness of the proposed mFILS.

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge: determining the reward model in the digital environment. This paper proposes a usability reward model in multi-agent reinforcement learning. Well-known mathematical formulas used for measuring usability metrics were analyzed in detail and incorporated into the usability reward model. In the usability reward model, any neural network-based multi-agent reinforcement learning algorithm can be used as the underlying learning algorithm. This paper presents a study using independent and actor-critic reinforcement learning algorithms to investigate their impact on the usability metrics of a mobile user interface. Computational experiments and usability tests were conducted in a specially designed multi-agent environment for mobile user interfaces, enabling the implementation of various usage scenarios and real-time adaptations.

Privacy-Preserving Efficient Federated-Learning Model Debugging

Federated learning allows large amounts of mobile clients to jointly construct a global model without sending their private data to a central server. A fundamental issue in this framework is the susceptibility to the erroneous training data. This problem is especially challenging due to the invisibility of clients’ local training data and training process, as well as the resource constraints. In this paper, we aim to solve this issue by introducing the first FL debugging framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLDebugger</i> , for mitigating test error caused by erroneous training data. The proposed solution traces the global model’s bugs (test errors), jointly through the training log and the underlying learning algorithm, back to first identify the clients and subsequently their training samples that are most responsible for the errors. In addition, we devise an influence-based participant selection strategy to fix bugs as well as to accelerate the convergence of model retraining. The performance of the identification algorithm is evaluated via extensive experiments on a real AIoT system (50 clients, including 20 edge computers, 20 laptops and 10 desktops) and in larger-scale simulated environments. The evaluation results attest to that our framework achieves accurate, privacy-preserving and efficient identification of negatively influential clients and samples, and significantly improves the model performance by fixing bugs.

Is the Corpus Ready for Machine Translation? A Case Study with Python to Pseudo-Code Corpus.

The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference (sim 10% on BLEU score) in the models’ performance after removing the issues from the corpus.

Property Checking with Interpretable Error Characterization for Recurrent Neural Networks

This paper presents a novel on-the-fly, black-box, property-checking through learning approach as a means for verifying requirements of recurrent neural networks (RNN) in the context of sequence classification. Our technique steps on a tool for learning probably approximately correct (PAC) deterministic finite automata (DFA). The sequence classifier inside the black-box consists of a Boolean combination of several components, including the RNN under analysis together with requirements to be checked, possibly modeled as RNN themselves. On one hand, if the output of the algorithm is an empty DFA, there is a proven upper bound (as a function of the algorithm parameters) on the probability of the language of the black-box to be nonempty. This implies the property probably holds on the RNN with probabilistic guarantees. On the other, if the DFA is nonempty, it is certain that the language of the black-box is nonempty. This entails the RNN does not satisfy the requirement for sure. In this case, the output automaton serves as an explicit and interpretable characterization of the error. Our approach does not rely on a specific property specification formalism and is capable of handling nonregular languages as well. Besides, it neither explicitly builds individual representations of any of the components of the black-box nor resorts to any external decision procedure for verification. This paper also improves previous theoretical results regarding the probabilistic guarantees of the underlying learning algorithm.

Anomaly Detection Aided Budget Online Classification for Imbalanced Data Streams

Learning from imbalanced data streams differs from the traditional learning paradigm due to the issues of imbalanced classes. It has significant implications in a myriad of real-world applications, ranging from financial risk, network security, to medical diagnosis. Moreover, outliers usually appear in data streams. The issue of class imbalance or anomaly itself could negatively affect the performance of the underlying learning algorithms, and their combination makes the learning problem harder. In this work, we propose an anomaly detection aided budget online weighted learning method (BOW-LM) to identify positive and negative instances from imbalanced data streams. BOW-LM is based on the widely used Feedforward Networks with Random Weights. An agile lightweight anomaly detector is designed based on the nonlinear mapping of the network. To reduce computational complexity and to response promptly, BOW-LM employs a matrix correction technique to update the learning model by only <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) operations for each data chunk with LL hidden layer nodes. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness of BOW-LM.

On experimenting large dataset for visualization using distributed learning and tree plotting techniques

Visualization as one major field making up data science has played significant roles in data exploration. With visualization at the center of every data analysis and application, exploratory analysis has proved the basis for which data analyst comparatively implement what-if scenario before and after processing. Interesting patterns generated from models visualized is very helpful in fast decision-making, model tuning and optimization. Although, conventional methods such as histogram, pie chart, box plot and bar graph are in most occasions not adequate enough to effectively convey the interesting pattern to be mined in large dataset. This paper therefore, presents a tree-plot approach which make use of an In-memory node mechanism from the h2o package to place the large dataset in memory. A Gradient Boosted Model (GBM) from same was implemented as the underlying learning algorithm to build the tree model, while the modeled trees were plotted using plotting techniques in data.tree. Execution process time, AUC, MSE and RMSE results obtained provide basis for evaluating how well the data was trained and for visualizing the modeled tree. It further substantial how a learner algorithm could work with a plotting method with less computational cost.

Exploiting Multilabel Information for Noise-Resilient Feature Selection

In a conventional supervised learning paradigm, each data instance is associated with one single class label. Multilabel learning differs in the way that data instances may belong to multiple concepts simultaneously, which naturally appear in a variety of high impact domains, ranging from bioinformatics and information retrieval to multimedia analysis. It targets leveraging the multiple label information of data instances to build a predictive learning model that can classify unlabeled instances into one or multiple predefined target classes. In multilabel learning, even though each instance is associated with a rich set of class labels, the label information could be noisy and incomplete as the labeling process is both time consuming and labor expensive, leading to potential missing annotations or even erroneous annotations. The existence of noisy and missing labels could negatively affect the performance of underlying learning algorithms. More often than not, multilabeled data often has noisy, irrelevant, and redundant features of high dimensionality. The existence of these uninformative features may also deteriorate the predictive power of the learning model due to the curse of dimensionality. Feature selection, as an effective dimensionality reduction technique, has shown to be powerful in preparing high-dimensional data for numerous data mining and machine-learning tasks. However, a vast majority of existing multilabel feature selection algorithms either boil down to solving multiple single-labeled feature selection problems or directly make use of the imperfect labels to guide the selection of representative features. As a result, they may not be able to obtain discriminative features shared across multiple labels. In this article, to bridge the gap between a rich source of multilabel information and its blemish in practical usage, we propose a novel noise-resilient multilabel informed feature selection framework (MIFS) by exploiting the correlations among different labels. In particular, to reduce the negative effects of imperfect label information in obtaining label correlations, we decompose the multilabel information of data instances into a low-dimensional space and then employ the reduced label representation to guide the feature selection phase via a joint sparse regression framework. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of the proposed MIFS framework.

Feature selection for entity extraction from multiple biomedical corpora: A PSO-based approach

Entity extraction is an important step in biomedical text mining. Among many other challenges, there are two very crucial issues, viz. determining the most applicable feature set so that the model can be precise and less complex, and adapting the system across multiple benchmark corpora. In this paper, we propose a novel method for feature selection using the search capability of particle swarm optimization. The compact feature set used for training the classifier yields much better results when compared to the baseline model, which was developed with a complete set of features. A large number of features suitable for named entity recognition task from biomedical domain are also developed in the current paper. The complete set of features is implemented by studying the properties of datasets and from the domain knowledge. We have used conditional random field, a robust classifier as the underlying learning algorithm which has shown success in solving similar kinds of problems. Our experiments on multiple benchmark corpora yield the level of performance which are at par the state-of-the-art techniques.

Active Learning in Black-Box Settings

Active learning refers to the settings in which a machine learning algorithm (learner) is able to select data from which it learns (selecting points and then obtaining their labels), and by doing so aims to achieve better accuracy (e.g., by avoiding obtaining training data that is redundant or unimportant). Active learning is particularly useful in cases where the labeling cost is high. A common assumption is that an active learning algorithm is aware of the details of the underlying learning algorithm for which it obtains the data. However, in many practical settings, obtaining precise details of the learning algorithm may not be feasible, making the underlying algorithm in essence a black box – no knowledge of the internal workings of the algorithm is available, and only the inputs and corresponding output estimates are accessible. This makes many of the traditional approaches not applicable, or at the least not effective. Hence our motivation is to use the only data that is accessible in black box settings – output estimates. We note that accuracy will improve only if the learner’s output estimates change. Therefore we propose active learning criterion that utilizes the information contained within the changes of output estimates.

Intelligent deflection routing in buffer-less networks.

Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched networks. The main goal of deflection routing is to successfully deflect a packet based only on a limited knowledge that network nodes possess about their environment. In this paper, we present a framework that introduces intelligence to deflection routing (iDef). iDef decouples the design of the signaling infrastructure from the underlying learning algorithm. It consists of a signaling and a decision-making module. Signaling module implements a feedback management protocol while the decision-making module implements a reinforcement learning algorithm. We also propose several learning-based deflection routing protocols, implement them in iDef using the ns-3 network simulator, and compare their performance.

An Efficient Method To Estimate Bagging‘s Generalization Error

Bagging (Breiman, 1994a) is a technique that tries to improve a learning algorithm‘s performance by using bootstrap replicates of the training set (Efron & Tibshirani, 1993, Efron, 1979). The computational requirements for estimating the resultant generalization error on a test set by means of cross-validation are often prohibitive, for leave-one-out cross-validation one needs to train the underlying algorithm on the order of mν times, where m is the size of the training set and ν is the number of replicates. This paper presents several techniques for estimating the generalization error of a bagged learning algorithm without invoking yet more training of the underlying learning algorithm (beyond that of the bagging itself), as is required by cross-validation-based estimation. These techniques all exploit the bias-variance decomposition (Geman, Bienenstock & Doursat, 1992, Wolpert, 1996). The best of our estimators also exploits stacking (Wolpert, 1992). In a set of experiments reported here, it was found to be more accurate than both the alternative cross-validation-based estimator of the bagged algorithm‘s error and the cross-validation-based estimator of the underlying algorithm‘s error. This improvement was particularly pronounced for small test sets. This suggests a novel justification for using bagging—more accurate estimation of the generalization error than is possible without bagging.

Tracking Context Changes through Meta-Learning

The article deals with the problem of learning incrementally (‘on-line’) in domains where the target concepts are context-dependent, so that changes in context can produce more or less radical changes in the associated concepts. In particular, we concentrate on a class of learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). A general two-level learning model is presented that effectively adjusts to changing contexts by trying to detect (via ‘meta-learning’) contextual clues and using this information to focus the learning process. Context learning and detection occur during regular on-line learning, without separate training phases for context recognition. Two operational systems based on this model are presented that differ in the underlying learning algorithm and in the way they use contextual information: METAL(B) combines meta-learning with a Bayesian classifier, while METAL(IB) is based on an instance-based learning algorithm. Experiments with synthetic domains as well as a number of ‘real-world’ problems show that the algorithms are robust in a variety of dimensions, and that meta-learning can produce substantial increases in accuracy over simple object-level learning in situations with changing contexts.