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: 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.
Example Output: Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification

Example Input: Artificial General Intelligence (AGI) is a field of research aiming to distill the principles of intelligence that operate independently of a specific problem domain or a predefined context and utilize these principles in order to synthesize systems capable of performing any intellectual task a human being is capable of and eventually go beyond that. While “narrow” artificial intelligence that focuses on solving specific problems such as speech recognition, text comprehension, visual pattern recognition, robotic motion, etc. has shown quite a few impressive breakthroughs lately, understanding general intelligence remains elusive. In this paper we offer a novel theoretical approach to understanding general intelligence. We start with a brief introduction of the current conceptual approach. Our critique exposes a number of serious limitations that are traced back to the ontological roots of the concept of intelligence. We then propose a paradigm shift from intelligence perceived as a competence of individual agents defined in relation to an a priori given problem domain or a goal, to intelligence perceived as a formative process of self-organization by which intelligent agents are individuated. We call this process open-ended intelligence. This paradigmatic shift significantly extends the concept of intelligence beyond its current conventional definitions and overcomes the difficulties exposed in the critique. Open-ended intelligence is developed as an abstraction of the process of cognitive development so its application can be extended to general agents and systems. We introduce and discuss three facets of the idea: the philosophical concept of individuation, sense-making – the bringing forth of a world of objects and relations, and the individuation of general cognitive agents in the light of the enactive approach to cognition and assemblage theory. We study these in order to establish in what sense formative individuating processes are indeed intelligent and why they are open-ended. We further show how open-ended intelligence can be framed in terms of a distributed, self-organizing network of interacting elements (i.e. a complex adaptive system) and how such a process is scalable. The framework highlights an important relation between coordination and intelligence and a new understanding of values. We conclude with a number of questions for future research.
Example Output: The individuation of Intelligent Agents

Example Input: Basics 5 Learning Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Task types and creation . . . . . . . . . . . . . . . . . . . . . . . 5 Further settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Accessing a learning task . . . . . . . . . . . . . . . . . . . . . . 10 Modifying a learning task . . . . . . . . . . . . . . . . . . . . . . 13 Example tasks and convenience functions . . . . . . . . . . . . . 15 Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Constructing a learner . . . . . . . . . . . . . . . . . . . . . . . . 15 Accessing a learner . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Modifying a learner . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Listing learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Training a Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Accessing learner models . . . . . . . . . . . . . . . . . . . . . . . 23 Further options and comments . . . . . . . . . . . . . . . . . . . 26 Predicting Outcomes for New Data . . . . . . . . . . . . . . . . . . . . 27 Accessing the prediction . . . . . . . . . . . . . . . . . . . . . . . 29 Adjusting the threshold . . . . . . . . . . . . . . . . . . . . . . . 33 Visualizing the prediction . . . . . . . . . . . . . . . . . . . . . . 35 Evaluating Learner Performance . . . . . . . . . . . . . . . . . . . . . 38 Available performance measures . . . . . . . . . . . . . . . . . . . 38 Listing measures . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Calculate performance measures . . . . . . . . . . . . . . . . . . 40 Access a performance measure . . . . . . . . . . . . . . . . . . . . 41 Binary classification . . . . . . . . . . . . . . . . . . . . . . . . . 42 Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Stratified resampling . . . . . . . . . . . . . . . . . . . . . . . . . 47
Example Output:
mlr Tutorial