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.
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Question: Class imbalance is one of the challenging problems for machine learning in many real-world applications, such as coal and gas burst accident monitoring: the burst premonition data is extreme smaller than the normal data, however, which is the highlight we truly focus on. Cost-sensitive adjustment approach is a typical algorithm-level method resisting the data set imbalance. For SVMs classifier, which is modified to incorporate varying penalty parameter(C) for each of considered groups of examples. However, the C value is determined empirically, or is calculated according to the evaluation metric, which need to be computed iteratively and time consuming. This paper presents a novel cost-sensitive SVM method whose penalty parameter C optimized on the basis of cluster probability density function(PDF) and the cluster PDF is estimated only according to similarity matrix and some predefined hyper-parameters. Experimental results on various standard benchmark data sets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used cost-sensitive techniques.

Answer: Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification


Question: Protein subcellular localization prediction is an important and challenging problem. The traditional biology experiments are expensive and time-consuming, so more and more research interests tend to a series of machine learning approaches for predicting protein subcellular location. There are two main difficult problems among the existing state-of-the-art methods. First, most of the existing techniques are designed to deal with the multi-class but not the multi-label classification, which ignores the connection between the multiple labels. In reality, multiple location proteins implicate that there are vital and unique biological significances worthy of special focus, which cannot be ignored. Second, the techniques for handling imbalanced data in multi-label classification problem is significant but less. For solving the two issues, we have developed an ensemble multi-label classifier called HPSLPred which can be applied for the multi-label classification with imbalanced protein source. For the conveniences of users, a user-friendly webserver for HPSLPred was established at http://server.malab.cn/HPSLPred.

Answer: HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source


Question: The multi-armed bandit problem (MBP) is the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards by referring to past experiences. Inspired by fluctuated movements of a rigid body in a tug-of-war game, we formulated a unique search algorithm that we call the ‘tug-of-war (TOW) dynamics’ for solving the MBP efficiently [1-5]. The cognitive medium access, which refers to multiuser channel allocations in cognitive radio, can be interpreted as the competitive multi-armed bandit problem (CMBP); the problem is to determine the optimal strategy for allocating channels to users which yields maximum total rewards gained by all users [6]. Here we show that it is possible to construct a physical device for solving the CMBP, which we call the ‘TOW Bombe’, by exploiting the TOW dynamics existed in coupled incompressible-fluid cylinders. This analog computing device achieves the ‘socially-maximum’ resource allocation that maximizes the total rewards in cognitive medium access without paying a huge computational cost that grows exponentially as a function of the problem size.

Answer:
Decision Maker using Coupled Incompressible-Fluid Cylinders