Above are papers related to the following paper: [Paper_ID] Paper #27246. 	[Title] Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms [Abstract] This paper presents the Plannett system, which combines artificial neural networks to achieve expert- level accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JAR- tool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110390. 	[Title] How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications [Abstract] This paper proposes a simple cost model for machine learning applications based on the notion of net present value. The model extends and unifies the models used in (Pazzani et al., 1994) and (Masand & Piatetsky-Shapiro, 1996). It attempts to answer the question "Should a given machine learning system now in the prototype stage be fielded?" The model's inputs are the system's confusion matrix, the cash flow matrix for the application, the cost per decision, the one-time cost of deploying the system, and the rate of return on investment. Like Provost and Fawcett's (1997) ROC convex hull method, the present model can be used for decision-making even when its input variables are not known exactly. Despite its simplicity, it has a number of non-trivial consequences. For example, under it the "no free lunch" theorems of learning theory no longer apply. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1955. 	[Title] In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning [Abstract] Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most machine learning techniques have been studied in the context of knowledge acquisition for well defined tasks, such as classification. Learning for these tasks can be handled by relatively simple algorithms. Complex domains present difficulties that can be approached by combining the strengths of several complementing learning techniques, and overcoming their weaknesses by providing alternative learning strategies. This study presents two perspectives, the macro and the micro, for viewing the issue of multistrategy learning. The macro perspective deals with the decomposition of an overall complex learning task into relatively well-defined learning tasks, and the micro perspective deals with designing multistrategy learning techniques for supporting the acquisition of knowledge for each task. The two perspectives are discussed in the context of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5966. 	[Title] Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm [Abstract] This paper introduces ICET, a new algorithm for costsensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for costsensitive classification EG2, CS-ID3, and IDX and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICETs search in bias space and discovers a way to improve the search. Please summarise all the given information.

Paper #27246 has the title `Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms
` and the abstract `This paper presents the Plannett system, which combines artificial neural networks to achieve expert- level accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JAR- tool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date. 
`.
Question: Which category should Paper #27246 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #27246 has the title `Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms
` and the abstract `This paper presents the Plannett system, which combines artificial neural networks to achieve expert- level accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JAR- tool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date. 
`.
Question: Which category should Paper #27246 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks