Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools

Dalaka, Anastasia; Kompare, Boris; Robnik–Šikonja, Marko; Sgardelis, Stefanos P.

doi:10.1016/s0304-3800(00)00237-4

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Similar studies are reported in King et al (1995) and Bratko et al (1998). Learning prediction models for various biological problems are reported in Debeljak et al (2001) for modelling the population of red deer, Blockeel et al (2004) for the prediction of biodegradation of chemicals, Kraakman (1998) for the prediction of characteristics of offspring of vegetables, Dalaka et al (2000) for the prediction of photosynthesis. Constructing and extending a classification of plants by clustering plant descriptions is presented in Alberdi & Sleeman (1997).…”

Section: Constructing a Model Of A Process Or Structuresupporting

confidence: 53%

Applications of machine learning: matching problems to tasks and methods

Someren

Urbančič

2005

The Knowledge Engineering Review

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The terminology of Machine Learning and Data Mining methods does not always allow a simple match between practical problems and methods. While some problems look similar from the user's point of view, but require different methods to be solved, some others look very different, yet they can be solved by applying the same methods and tools. Choosing appropriate Machine Learning methods for problem solving in practice is therefore largely a matter of experience and it is not realistic to expect a simple look-up table with matches between problems and methods. However, some guidelines can be given and a collection that summarizes other people's experience can also be helpful. A small number of definitions characterize the tasks that are performed by a large proportion of methods. Most of the variation in methods is concerned with differences in data types and algorithmic aspects of methods. In this paper, we summarize the main task types and illustrate how a wide variety of practical problems are formulated in terms of these tasks. The match between problems and tasks is illustrated with a collection of example applications with the aim of helping to express new practical problems as Machine Learning tasks. Some tasks can be decomposed into subtasks, allowing a wider variety of matches between practical problems and (combinations of) methods. We review the main principles for choosing between alternatives and illustrate this with a large collection of applications. We believe that this provides some guidelines.

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Section: Constructing a Model Of A Process Or Structuresupporting

confidence: 53%

Applications of machine learning: matching problems to tasks and methods

Someren

Urbančič

2005

The Knowledge Engineering Review

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“…To summarize our results, we used regression trees that predicted either extinction threshold or modified extinction rate, ρ, according to habitat, disturbance, and dispersal. Regression trees have been applied to the analysis of ecological data (e.g., Dalaka et al 2000; De'ath & Fabricius 2000). They predict the value of a response variable from the values of a set of explanatory variables that may be either numerical or categorical.…”

Section: Methodsmentioning

confidence: 99%

Metapopulation Extinction Risk under Spatially Autocorrelated Disturbance

Kallimanis

Kunin

Halley

et al. 2005

Conservation Biology

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Recent extinction models generally show that spatial aggregation of habitat reduces overall extinction risk because sites emptied by local extinction are more rapidly recolonized. We extended such an investigation to include spatial structure in the disturbance regime. A spatially explicit metapopulation model was developed with a wide range of dispersal distances. The degree of aggregation of both habitat and disturbance pattern could be varied from a random distribution, through the intermediate case of a fractal distribution, all the way to complete aggregation (single block). Increasing spatial aggregation of disturbance generally increased extinction risk. The relative risk faced by populations in different landscapes varied greatly, depending on the disturbance regime. With random disturbance, the spatial aggregation of habitat reduced extinction risk, as in earlier studies. Where disturbance was spatially autocorrelated, however, this advantage was eliminated or reversed because populations in aggregated habitats are at risk of mass extinction from coarse-scale disturbance events. The effects of spatial patterns on extinction risk tended to be reduced by long-distance dispersal. Given the high levels of spatial correlation in natural and anthropogenic disturbance processes, population vulnerability may be greatly underestimated both by classical (nonspatial) models and by those that consider spatial structure in habitat alone.Resumen: Los modelos recientes de extinción generalmente muestran que la agregación espacial de hábitat reduce el riesgo de extinción debido a una recolonización más rápida de sitios vacíos por extinción local. Extendimos la investigación para incluir la estructura espacial en el régimen de perturbación. Desarrollamos un modelo metapoblacional espacialmente explícito en el que el patrón espacial tanto del hábitat como de los regímenes de perturbación podía variar aleatoriamente de fractal a completamente agregado (bloque) y con una amplia gama de distancias de dispersión. El incremento de la agregación espacial de la perturbación generalmente incrementó el riesgo de extinción. El riesgo relativo que enfrentan poblaciones en paisajes diferentes fue muy variable, dependiendo del régimen de perturbación. Con perturbación aleatoria, la agregación espacial de hábitat redujo el riesgo de extinción, como en estudios anteriores. Sin embargo, cuando la perturbación estaba autocorrelacionada espacialmente, esta ventaja se eliminaba o invertía debido a que las poblaciones en hábitats agregados están en riesgo de extinción masiva por eventos perturbadores a escala gruesa. Los efectos de patrones espaciales sobre el riesgo de extinción tendieron a reducirse por la dispersión de larga distancia. Debido a los altos niveles de correlación espacial en los procesos naturales y humanos de perturbación, la vulnerabilidad puede estar enormemente subestimada tanto por modelos clásicos (no espaciales) como por los que sólo consideran la estructura espacial del habitat. Los modelos que consideran la estructu...

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“…Only the most promising attributes are selected for construction and various operators were applied on them (conjunction, disjunction, summation, product). The results are good and in some domains the obtained constructs provided additional insight into the domain (Dalaka et al, 2000).…”

Section: Building Tree Based Modelsmentioning

confidence: 80%

“…ReliefF originally used constant influence of k nearest neighbors with k set to some small number (usually 10). We believe that the former approach is less risky (as it turned out in a real world application (Dalaka et al, 2000)) because as we are taking more near neighbors we reduce the risk of the following pathological case: we have a large number of instances and a mix of nominal and numerical attributes where numerical attributes prevail; it is possible that all the nearest neighbors are closer than 1 so that there are no nearest neighbors with differences in values of a certain nominal attribute. If this happens in a large part of the problem space this attribute gets zero weight (or at least small and unreliable one).…”

Section: Taking Distance Into Accountmentioning

confidence: 99%

Untitled

2003

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Abstract. Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming.A broad spectrum of successful uses calls for especially careful investigation of various features Relief algorithms have. In this paper we theoretically and empirically investigate and discuss how and why they work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.

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Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools

Cited by 18 publications

References 17 publications

Applications of machine learning: matching problems to tasks and methods

Applications of machine learning: matching problems to tasks and methods

Metapopulation Extinction Risk under Spatially Autocorrelated Disturbance

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