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BACKGROUND Because effective prediction of survival time can be highly beneficial for the treatment of glioblastoma patients, the relationship between survival time and multiple patient characteristics has been investigated. In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI(More)
Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of training cases can impact the performance of the resulting classifier as well as properties of the(More)
The quantity of rooftop solar photovoltaic (PV) installations has grown rapidly in the US in recent years. There is a strong interest among decision makers in obtaining high quality information about rooftop PV, such as the locations, power capacity, and energy production of existing rooftop PV installations. Solar PV installations are typically connected(More)
In this paper the effect of class imbalance in the case base of a case-based classifier is investigated as it pertains to case base reduction and the resulting classifier performance. A k-nearest neighbor algorithm is used as a classifier and the Random Mutation Hill Climbing (RMHC) algorithm is used for case base reduction. The effects at various levels of(More)
Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine(More)
Optimal management of anemia due to End-Stage Renal Disease (ESRD) is a challenging task to physicians due to large inter-subject variability in response to Erythropoiesis Stimulating Agents (ESA). We demonstrate that an optimal dosing strategy for ESA can be derived using Reinforcement Learning (RL) techniques. In this study, we show some preliminary(More)
Computational Intelligence-Machine Learning (CIML) is a mature discipline with a wide range of potential applications. Many state-of-the-art methods developed in CIML, however, remain unused outside the relatively small groups of researchers in which they originate. This can be attributed to lack of communication between disciplines, difficulty of use,(More)
When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to(More)
—Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote sensing modality for buried target detection (e.g., landmines). In this context, raw FLGPR data is beamformed into images and then computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning(More)