Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Evolution and conservation of immunological activity

Paraphrasing what Gregory Bateson says on evolution, we might say that: "Immunology has long been badly taught. In particular, students - and even professional immunologists - acquire theories of immunological activity without any deep understanding of what problems these theories attempt to solve."

Classification and hearing evolution of patients with sudden sensorineural hearing loss

The aim of this study was to analyze clinical aspects, hearing evolution and efficacy of clinical treatment of patients with sudden sensorineural hearing loss (SSNHL). This was a prospective clinical study of 136 consecutive patients with SSNHL divided into three groups after diagnostic evaluation: patients with defined etiology (DE, N = 13, 10%), concurrent diseases (CD, N = 63, 46.04%) and idiopathic sudden sensorineural hearing loss (ISSHL, N = 60, 43.9%). Initial treatment consisted of prednisone and pentoxifylline. Clinical aspects and hearing evolution for up to 6 months were evaluated. Group CD comprised 73% of patients with metabolic decompensation in the initial evaluation and was significantly older (53.80 years) than groups DE (41.93 years) and ISSHL (39.13 years). Comparison of the mean initial and final hearing loss of the three groups revealed a significant hearing improvement for group CD (P = 0.001) and group ISSHL (P = 0.001). Group DE did not present a significant difference in thresholds. The clinical classification for SSNHL allows the identification of significant differences regarding age, initial and final hearing impairment and likelihood of response to therapy. Elevated age and presence of coexisting disease were associated with a greater initial hearing impact and poorer hearing recovery after 6 months. Patients with defined etiology presented a much more limited response to therapy. The occurrence of decompensated metabolic and cardiovascular diseases and the possibility of first manifestation of auto-immune disease and cerebello-pontine angle tumors justify an adequate protocol for investigation of SSNHL.

Comparison of I-FISH and G-banding for the detection of chromosomal abnormalities during the evolution of myelodysplastic syndrome

Myelodysplastic syndrome (MDS) patients with a normal karyotype constitute a heterogeneous group from a biological standpoint and their outcome is often unpredictable. Interphase fluorescence in situ hybridization (I-FISH) studies could increase the rate of detection of abnormalities, but previous reports in the literature have been contradictory. We performed I-FISH and conventional karyotyping (G-banding) on 50 MDS patients at diagnosis, after 6 and 12 months or at any time if a transformation to acute myeloid leukemia (AML) was detected. Applying a probe-panel targeting the centromere of chromosomes 7 and 8, 5q31, 5p15.2 and 7q31, we observed one case with 5q deletion not identified by G-banding. I-FISH at 6 and 12 months confirmed the karyotype results. Eight cases transformed to AML during follow-up, but no hidden clone was detected by I-FISH in any of them. The inclusion of I-FISH during follow-up of MDS resulted in a small improvement in abnormality detection when compared with conventional G-banding.

Evolution of the Finnish military doctrine, 1945-1985

Finnish Defence Studies is published under the auspices of the War College, and the contributions reflect the fields of research and teaching of the College. Finnish Defence Studies will occasionally feature documentation on Finnish Security Policy. Views expressed are those of the authors and do not necessarily imply endorsement by the War College.

Creating SHAREDVALUE: A How-to Guide for the New Corporate (R)evolution

Competitividad y valor compartido