The fundamental problem in short-text classification is \emph{feature sparseness} -- the lack of feature overlap between a trained model and a test instance to be classified. We propose \emph{ClassiNet} -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex $v_i$ in the ClassiNet where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex $v_j$ represents the conditional probability that given $v_i$ exists in an instance, $v_j$ also exists in the same instance. We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance $\vec{x}$, we find similar features from ClassiNet that did not appear in $\vec{x}$, and append those features in the representation of $\vec{x}$. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.

The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.

Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original formulation of graph-based SSL and prove the supermodularity of an AG-SSL objective function under a broad class of regularization functions parameterized by Stieltjes matrices. Under this setting, supermodularity yields a novel greedy label sampling algorithm with guaranteed performance relative to the optimal sampling set. Compared to three state-of-the-art graph signal sampling and recovery methods on two real-life community detection datasets, the proposed AG-SSL method attains superior classification accuracy given limited sample budgets.

Machine Learning (ML) algorithms are now used in a wide range of application domains in society. Naturally, software implementations of these algorithms have become ubiquitous. Faults in ML software can cause substantial losses in these application domains. Thus, it is very critical to conduct effective testing of ML software to detect and eliminate its faults. However, testing ML software is difficult, partly because producing test oracles used for checking behavior correctness (such as using expected properties or expected test outputs) is challenging. In this paper, we propose an approach of multiple-implementation testing to test supervised learning software, a major type of ML software. In particular, our approach derives a test input's proxy oracle from the majority-voted output running the test input of multiple implementations of the same algorithm (based on a pre-defined percentage threshold). Our approach reports likely those test inputs whose outputs (produced by an implementation under test) are different from the majority-voted outputs as failing tests. We evaluate our approach on two highly popular supervised learning algorithms: k-Nearest Neighbor (kNN) and Naive Bayes (NB). Our results show that our approach is highly effective in detecting faults in real-world supervised learning software. In particular, our approach detects 13 real faults and 1 potential fault from 19 kNN implementations and 16 real faults from 7 NB implementations. Our approach can even detect 7 real faults and 1 potential fault among the three popularly used open-source ML projects (Weka, RapidMiner,  and KNIME).

This is our first post in this sub series "Machine Learning Type" under master series "Machine Learning Explained". We will only talk about supervised machine learning in details here. Machine learning algorithms "learns" from the observations. When exposed to more observations, the algorithm improves its predictive performance. Supervised Learning is becoming a good friend for marketing business in particular.

The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples. In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are fewer. For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.

Information planning enables faster learning with fewer training examples. It is particularly applicable when training examples are costly to obtain. This work examines the advantages of information planning for text data by focusing on three supervised models: Naive Bayes, supervised LDA and deep neural networks. We show that planning based on entropy and mutual information outperforms random selection baseline and therefore accelerates learning.

Cryo-electron microscopy (cryoEM) is fast becoming the preferred method for protein structure determination. Particle picking is a significant bottleneck in the solving of protein structures from single particle cryoEM. Hand labeling sufficient numbers of particles can take months of effort and current computationally based approaches are often ineffective. Here, we frame particle picking as a positive-unlabeled classification problem in which we seek to learn a convolutional neural network (CNN) to classify micrograph regions as particle or background from a small number of labeled positive examples and many unlabeled examples. However, model fitting with very few labeled data points is a challenging machine learning problem. To address this, we develop a novel objective function, GE-binomial, for learning model parameters in this context. This objective uses a newly-formulated generalized expectation criteria to learn effectively from unlabeled data when using minibatched stochastic gradient descent optimizers. On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples. Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available. We also compare our GE-binomial method with other positive-unlabeled learning methods never before applied to particle picking. We expect our particle picking tool, Topaz, based on CNNs trained with GE-binomial, to be an essential component of single particle cryoEM analysis and our GE-binomial objective function to be widely applicable to positive-unlabeled classification problems.

The media update team explores the topic. Supervised learning can be defined as a type of machine learning algorithm that relies on a training dataset to make predictions. Breaking it down to basics, supervised machine learning is when a system receives a training dataset made up of input data and corresponding output data. From the training data, the system learns how the input led to the output data, creating a model – or what is called a'mapping function'. It can then be given different input data to predict what the output would be, based on the patterns it recognised in the training set it has learnt from.

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local neighborhood and a fully-connected layer. Perhaps surprisingly, we show that a linear model, that removes all the intermediate fully-connected layers, is still able to achieve a performance comparable to the state-of-the-art models. This significantly reduces the number of parameters, which is critical for semi-supervised learning where number of labeled examples are small. This in turn allows a room for designing more innovative propagation layers. Based on this insight, we propose a novel graph neural network that removes all the intermediate fully-connected layers, and replaces the propagation layers with attention mechanisms that respect the structure of the graph. The attention mechanism allows us to learn a dynamic and adaptive local summary of the neighborhood to achieve more accurate predictions. In a number of experiments on benchmark citation networks datasets, we demonstrate that our approach outperforms competing methods. By examining the attention weights among neighbors, we show that our model provides some interesting insights on how neighbors influence each other.