Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers.
A generative model is also presented in @cite_16 . Here, the main assumption is that words in documents belonging to several categories can be characterized as a mixture of characteristic words related to each of the categories, being this assumption confirmed with experimentation. Both first (PMM1) and second (PMM2) order models are built, and learning algorithms (using a MAP estimation) are proposed for both alternatives. Experiments are carried out with webpages gathered from the yahoo.com server. Presented results are good, and improve other methods as SVM, naive Bayes and @math -NN.
@cite_16
2129414564 2103170111 106244805 2593887162
We propose probabilistic generative models, called parametric mixture models (PMMs), for multiclass, multi-labeled text categorization problem. Conventionally, the binary classification approach has been employed, in which whether or not text belongs to a category is judged by the binary classifier for every category. In contrast, our approach can simultaneously detect multiple categories of text using PMMs. We derive efficient learning and prediction algorithms for PMMs. We also empirically show that our method could significantly outperform the conventional binary methods when applied to multi-labeled text categorization using real World Wide Web pages. We present an inference algorithm that organizes observed words (tokens) into structured inflectional paradigms (types). It also naturally predicts the spelling of unobserved forms that are missing from these paradigms, and discovers inflectional principles (grammar) that generalize to wholly unobserved words. Our Bayesian generative model of the data explicitly represents tokens, types, inflections, paradigms, and locally conditioned string edits. It assumes that inflected word tokens are generated from an infinite mixture of inflectional paradigms (string tuples). Each paradigm is sampled all at once from a graphical model, whose potential functions are weighted finite-state transducers with language-specific parameters to be learned. These assumptions naturally lead to an elegant empirical Bayes inference procedure that exploits Monte Carlo EM, belief propagation, and dynamic programming. Given 50--100 seed paradigms, adding a 10-million-word corpus reduces prediction error for morphological inflections by up to 10 . Abstract : Most of the popular topic models (such as Latent Dirichlet Allocation) have an underlying assumption: bag of words. However, text is indeed a sequence of discrete word tokens, and without considering the order of words (in another word, the nearby context where a word is located), the accurate meaning of language cannot be exactly captured by word co-occurrences only. In this sense, collocations of words (phrases) have to be considered. However, like individual words, phrases sometimes show polysemy as well depending on the context. More noticeably, a composition of two (or more) words is a phrase in some contexts, but not in other contexts. In this paper, the authors propose a new probabilistic generative model that automatically determines unigram words and phrases based on context and simultaneously associates them with a mixture of topics. They present very interesting results on large text corpora. We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerful than their bag-of-words ancestors (e.g., they account for conditional dependencies across words in a document), they have higher asymptotic error rates than discriminatively trained RNN models. However we also find that generative models approach their asymptotic error rate more rapidly than their discriminative counterparts---the same pattern that Ng & Jordan (2001) proved holds for linear classification models that make more naive conditional independence assumptions. Building on this finding, we hypothesize that RNN-based generative classification models will be more robust to shifts in the data distribution. This hypothesis is confirmed in a series of experiments in zero-shot and continual learning settings that show that generative models substantially outperform discriminative models.