Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.

Case base reduction using solution-space metrics

In this paper we propose a case base reduction technique which uses a metric defined on the solution space. The technique utilises the Generalised Shepard Nearest Neighbour (GSNN) algorithm to estimate nominal or real valued solutions in case bases with solution space metrics. An overview of GSNN and a generalised reduction technique, which subsumes some existing decremental methods, such as the Shrink algorithm, are presented. The reduction technique is given for case bases in terms of a measure of the importance of each case to the predictive power of the case base. A trial test is performed on two case bases of different kinds, with several metrics proposed in the solution space. The tests show that GSNN can out-perform standard nearest neighbour methods on this set. Further test results show that a caseremoval order proposed based on a GSNN error function can produce a sparse case base with good predictive power.

Impacts of wildlife on household food security and income in northeastern Tanzania

We evaluated the impacts of wildlife on household food security and income in three semi-arid villages adjacent to Lake Manyara National Park (LMNP) and Mkomazi Game Reserve (MGR) in Northeastern Tanzania. Survey data were collected using both household interviews and human-wildlife conflict related archive information from the village government offices. Crop destruction by wildlife influenced both household food security and cash income. Crop damage to households was, on average, 0.08 ton/annum, equivalent to two months household loss of food and reduced household cash income by 1.3%. A combination of measures is proposed as incentives for conservation. These include provision of economic incentives, soft loans to initiate non-farm (e.g., ecotourism, business enterprises) projects to ease dependency on natural resources, increasing of reserves buffer zones and fencing of reserves.