In this paper, we propose to study the problem of identifying and classifying tweets into intent categories. For example, a tweet “I wanna buy a new car” indicates the user’s intent for buying a car. Identifying such intent tweets will have great commercial value among others. In particular, it is important that we can distinguish different types of intent tweets. We propose to classify intent tweets into six categories, namely Food & Drink, Travel, Career & Education, Goods & Services, Event and Activities and Trifle. We propose a semisupervised learning approach to categorizing intent tweets into the six categories.We construct a test collection by using a bootstrap method. Our experimental results show that our approach is effective in inferring intent categories for tweets.

A known limitation of many diagnosis algorithms is that the number of diagnoses they return can be very large. This raises the question of how to use such a large set of diagnoses. For example, presenting hundreds of diagnoses to a human operator (charged with repairing the system) is meaningless. In various settings, including decision support for a human operator and automated troubleshooting processes, it is sufficient to be able to answer a basic diagnostic question: is a given component faulty? We propose a way to aggregate an arbitrarily large set of diagnoses to return an estimate of the likelihood of a given component to be faulty. The resulting mapping of components to their likelihood of being faulty is called the system's health state. We propose two metrics for evaluating the accuracy of a health state and show that an accurate health state can be found without finding all diagnoses. An empirical study explores the question of how many diagnoses are needed to obtain an accurate enough health state, and a simple online stopping criterion is proposed.

Learning from imbalanced data sets is one of the challenging problems in machine learning, which means the number of negative examples is far more than that of positive examples. The main problems of existing methods are: (1) The degree of re-sampling, a key factor greatly affecting performance, needs to be pre-fixed, which is difficult to make the optimal choice; (2) Many useful negative samples are discarded in under-sampling; (3) The effectiveness of algorithm-level methods are limited because they just use the original training data for single classifier. To address the above issues, a novel approach of adaptive sampling with optimal cost is proposed for class-imbalance learning in this paper. The novelty of the proposed approach mainly lies in: adaptively over-sampling the minority positive examples and under-sampling the majority negative examples, forming different sub-classifiers by different subsets of training data with the best cost ratio adaptively chosen, and combining these sub-classifiers according to their accuracy to create a strong classifier. It aims to make full use of the whole training data and improve the performance of class-imbalance learning classifier. The solid experiments are conducted to compare the performance between the proposed approach and 12 state-of-the-art methods on challenging 16 UCI data sets on 3 evaluation metrics, and the results show the proposed approach can achieve superior performance in class-imbalance learning.

We present an unsupervised approach for abnormal event detection in videos. We propose, given a dictionary of features learned from local spatiotemporal cuboids using the sparse coding objective, the abnormality of an event depends jointly on two factors: the frequency of each feature in reconstructing all events (or, rarity of a feature) and the strength by which it is used in reconstructing the current event (or, the absolute coefficient). The Incremental Coding Length (ICL) of a feature is a measure of its entropy gain. Given a dictionary, the ICL computation does not involve any parameter, is computationally efficient and has been used for saliency detection in images with impressive results. In this paper, the rarity of a dictionary feature is learned online as its average energy, a function of its ICL. The proposed approach is applicable to real world streaming videos. Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art.

In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underpinning. In practical recommendation, only items at the top of the ranked list are presented to the users. With this in mind we propose a class of objective functions which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list. This loss is differentiable and is optimised through a carefully designed stochastic gradient-descent-based algorithm which scales linearly with the size of the data. We mitigate sample bias present in the data by sampling observations according to a certain power-law based distribution. In addition, we provide computation results as to the efficacy of the proposed method using synthetic and real data.

As the proliferation of constant data feeds increases from social media, embedded sensors, and other sources, the capability to provide predictive concept labels to these data streams will become ever more important and lucrative. However, the dynamic, non-stationary nature, and effectively infinite length of data streams pose additional challenges for stream data mining algorithms. The sparse quantity of training data also limits the use of algorithms that are heavily dependent on supervised training. To address all these issues, we propose an incremental semi-supervised method that provides general concept class label predictions, but it also tracks concept clusters within the feature space using an innovative new online clustering algorithm. Each concept cluster contains an embedded stream classifier, creating a diverse ensemble for data instance classification within the generative model used for detecting emerging concepts in the stream. Unlike other recent novel class detection methods, our method goes beyond detecting, and continues to differentiate and track the emerging concepts. We show the effectiveness of our method on several synthetic and real world data sets, and we compare the results against other leading baseline methods.

There are many criteria for measuring the value of information (VOI), each based on a different principle that is usually suitable for specific applications. We propose a new criterion for measuring the value of information, which values information that leads to robust decisions (i.e., ones that are unlikely to change due to new information). We also introduce an algorithm for Naive Bayes networks that selects features with maximal VOI under the new criteria. We discuss the application of the new criteria to classification tasks, showing how it can be used to tradeoff the budget, allotted for acquiring information, with the classification accuracy. In particular, we show empirically that the new criteria can reduce the expended budget significantly while reducing the classification accuracy only slightly. We also show empirically that the new criterion leads to decisions that are much more robust than those based on traditional VOI criteria, such as information gain and classification loss. This make the new criteria particularly suitable for certain decision making applications.

The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little attention has been devoted to the development of data representation that can be used to improve a user's ability to interpret the data and machine learning models to solve real-world problems. In this paper, we quantitatively and qualitatively evaluate an efficient, accurate and scalable feature-compression method using latent Dirichlet allocation for discrete data. This representation can effectively communicate the characteristics of high-dimensional, complex data points. We show that the improvement of a user's interpretability through the use of a topic modeling-based compression technique is statistically significant, according to a number of metrics, when compared with other representations. Also, we find that this representation is scalable --- it maintains alignment with human classification accuracy as an increasing number of data points are shown. In addition, the learned topic layer can semantically deliver meaningful information to users that could potentially aid human reasoning about data characteristics in connection with compressed topic space.

The potential benefits of mining social media to learn about adverse drug reactions (ADRs) are rapidly increasing with the increasing popularity of social media. Unknown ADRs have traditionally been discovered by expensive post-marketing trials, but recent work has suggested that some unknown ADRs may be discovered by analyzing social media. We propose three methods for extracting ADRs from forum posts and tweets, and compare our methods with several existing methods. Our methods outperform the existing methods in several scenarios; our filtering method achieves the highest F1 and precision on forum posts, and our CRF method achieves the highest precision on tweets. Furthermore, we address the difficulty of annotating social media on a large scale with an alternate evaluation scheme that takes advantage of the ADRs listed on drug labels. We investigate how well this alternate evaluation approximates a traditional evaluation using human annotations.

Online learning from imbalanced streaming data to capture the nonlinearity and heterogeneity of the data is significant in machine learning and data mining. To tackle this problem, we propose a kernelized online imbalanced learning (KOIL) algorithm to directly maximize the area under the ROC curve (AUC). We address two more challenges: 1) How to control the number of support vectors without sacrificing model performance; and 2) how to restrict the fluctuation of the learned decision function to attain smooth updating. To this end, we introduce two buffers with fixed budgets (buffer sizes) for positive class and negative class, respectively, to store the learned support vectors, which can allow us to capture the global information of the decision boundary. When determining the weight of a new support vector, we confine its influence only to its $k$-nearest opposite support vectors. This can restrict the effect of new instances and prevent the harm of outliers. More importantly, we design a sophisticated scheme to compensate the model after replacement is conducted when either buffer is full. With this compensation, the learned model approaches the one learned with infinite budgets. We present both theoretical analysis and extensive experimental comparison to demonstrate the effectiveness of our proposed KOIL.