The number of missing people (i.e., people who get lost) greatly increases in recent years. It is a serious worldwide problem, and finding the missing people consumes a large amount of social resources. In tracking and finding these missing people, timely data gathering and analysis actually play an important role. With the development of social media, information about missing people can get propagated through the web very quickly, which provides a promising way to solve the problem. The information in online social media is usually of heterogeneous categories, involving both complex social interactions and textual data of diverse structures. Effective fusion of these different types of information for addressing the missing people identification problem can be a great challenge. Motivated by the multi-instance learning problem and existing social science theory of "homophily", in this paper, we propose a novel r-instance (RI) learning model.
When the features are sparse, it's difficult for the single classifier to achieve good prediction of short text classification. Researchers propose ensemble learning methods to get a good performance. By assigning a proper weight for each weak classifier, ensemble learning methods get the weight of each feature. In this case, we can learn an ensemble model to solve the short text classification problem. Yan etc. @cite_10 propose a new dynamic assembly classification algorithm for short text. To solve the problems of sparse features and unbalanced data of the short text, a treelike assembly classifier is constructed to support the classification. Then they present a dynamic strategy to adjust the combinational structure in an adaptive way. Using very few words, Aixin Sun @cite_14 proposed a short text classification method. The majority vote of the search results determines the predicted category label in this method. The results can be derived by searching for a small set of labeled short texts best matching the query words.
@cite_14 @cite_10
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Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class. We propose a simple, scalable, and non-parametric approach for short text classification. Leveraging the well studied and scalable Information Retrieval (IR) framework, our approach mimics human labeling process for a piece of short text. It first selects the most representative and topical-indicative words from a given short text as query words, and then searches for a small set of labeled short texts best matching the query words. The predicted category label is the majority vote of the search results. Evaluated on a collection of more than 12K Web snippets, the proposed approach achieves comparable classification accuracy with the baseline Maximum Entropy classifier using as few as 3 query words and top-5 best matching search hits. Among the four query word selection schemes proposed and evaluated in our experiments, term frequency together with clarity gives the best classification accuracy. This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from large-scale data collections. The main motivation of this work is that many classification tasks working with short segments of text & Web, such as search snippets, forum & chat messages, blog & news feeds, product reviews, and book & movie summaries, fail to achieve high accuracy due to the data sparseness. We, therefore, come up with an idea of gaining external knowledge to make the data more related as well as expand the coverage of classifiers to handle future data better. The underlying idea of the framework is that for each classification task, we collect a large-scale external data collection called "universal dataset", and then build a classifier on both a (small) set of labeled training data and a rich set of hidden topics discovered from that data collection. The framework is general enough to be applied to different data domains and genres ranging from Web search results to medical text. We did a careful evaluation on several hundred megabytes of Wikipedia (30M words) and MEDLINE (18M words) with two tasks: "Web search domain disambiguation" and "disease categorization for medical text", and achieved significant quality enhancement. We study resource-limited online learning, motivated by the problem of conditional-branch outcome prediction in computer architecture. In particular, we consider (parallel) time and space-efficient ensemble learners for online settings, empirically demonstrating benefits similar to those shown previously for offline ensembles. Our learning algorithms are inspired by the previously published “boosting by filtering” framework as well as the offline Arc-x4 boosting-style algorithm. We train ensembles of online decision trees using a novel variant of the ID4 online decision-tree algorithm as the base learner, and show empirical results for both boosting and bagging-style online ensemble methods. Our results evaluate these methods on both our branch prediction domain and online variants of three familiar machine-learning benchmarks. Our data justifies three key claims. First, we show empirically that our extensions to ID4 significantly improve performance for single trees and additionally are critical to achieving performance gains in tree ensembles. Second, our results indicate significant improvements in predictive accuracy with ensemble size for the boosting-style algorithm. The bagging algorithms we tried showed poor performance relative to the boosting-style algorithm (but still improve upon individual base learners). Third, we show that ensembles of small trees are often able to outperform large single trees with the same number of nodes (and similarly outperform smaller ensembles of larger trees that use the same total number of nodes). This makes online boosting particularly useful in domains such as branch prediction with tight space restrictions (i.e., the available real-estate on a microprocessor chip).