In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximumcut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.
Example Output: A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure

Example Input: Automated learning of patients' demographics can be seen as multilabel problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where they can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multilabel learning algorithms. The best performing multi label ensembles include BR/Hoeffding Tree, CC/Hoeffding Tree, BCC/Hoeffding Tree, BR/JRIP, CC/JRIP, BCC/ JRIP respectively. In the empirical part of this study, we used on the UCI Diabetics dataset of over 100,000 records, collected from 130 US hospitals. The dataset consisted of attributes that included personal demographics, diagnoses code, lab results, etc. Experiments conducted on datasets of 1000, 10000, 20000 samples, show that BR/JRip model achieves a high overall accuracy of 0.533 (1000 samples), 0.702 (10000 samples), 0.569 (20000 samples), improving over the baseline model ZeroR with accuracy of 0.526, 0.586, .562 respectively. Loss functions such as Rank Loss, One Error, Hamming Loss, and Zero One Loss are also low for BCC/JRIP model for all samples of dataset, making it the best candidate for better performance given the label dependencies.
Example Output: Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients

Example Input: A long-standing challenge in coreference resolution has been the incorporation of entity-level information features defined over clusters of mentions instead of mention pairs. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters. Using these representations, our system learns when combining clusters is desirable. We train the system with a learning-to-search algorithm that teaches it which local decisions (cluster merges) will lead to a high-scoring final coreference partition. The system substantially outperforms the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task dataset despite using few hand-engineered features.
Example Output:
Improving Coreference Resolution by Learning Entity-Level Distributed Representations