IIIA-Artificial Intelligence Research Institute, Spanish National Research Council, Campus UAB s/n, Bellaterra, Catalonia, Spain.
Artif Intell Med. 2011 Feb;51(2):93-105. doi: 10.1016/j.artmed.2010.09.001. Epub 2010 Nov 12.
Early diagnosis of melanoma is based on the ABCD rule which considers asymmetry, border irregularity, color variegation, and a diameter larger than 5mm as the characteristic features of melanomas. When a skin lesion presents these features it is excised as prevention. Using a non-invasive technique called dermoscopy, dermatologists can give a more accurate evaluation of skin lesions, and can therefore avoid the excision of lesions that are benign. However, dermatologists need to achieve a good dermatoscopic classification of lesions prior to extraction. In this paper we propose a procedure called LazyCL to support dermatologists in assessing the classification of skin lesions. Our goal is to use LazyCL for generating a domain theory to classify melanomas in situ.
To generate a domain theory, the LazyCL procedure uses a combination of two artificial intelligence techniques: case-based reasoning and clustering. First LazyCL randomly creates clusters and then uses a lazy learning method called lazy induction of descriptions (LID) with leave-one-out on them. By means of LID, LazyCL collects explanations of why the cases in the database should belong to a class. Then the analysis of relationships among explanations produces an understandable clustering of the dataset. After a process of elimination of redundancies and merging of clusters, the set of explanations is reduced to a subset of it describing classes that are "almost" discriminant. The remaining explanations form a preliminary domain theory that is the basis on which experts can perform knowledge discovery.
We performed two kinds of experiments. First ones consisted on using LazyCL on a database containing the description of 76 melanomas. The domain theory obtained from these experiments was compared on previous experiments performed using a different clustering method called self-organizing maps (SOM). Results of both methods, LazyCL and SOM, were similar. The second kind of experiments consisted on using LazyCL on well known domains coming from the machine learning repository of the Irvine University. Thus, since these domains have known solution classes, we can prove that the clusters build by LazyCL are correct.
We can conclude that LazyCL that uses explained case-based reasoning for knowledge discovery is feasible for constructing a domain theory. On one hand, experiments on the melanoma database show that the domain theory build by LazyCL is easy to understand. Explanations provided by LID are easily understood by domain experts since these descriptions involve the same attributes than they used to represent domain objects. On the other hand, experiments on standard machine learning data sets show that LazyCL is a good method of clustering since all clusters produced are correct.
黑色素瘤的早期诊断基于 ABCD 规则，该规则将不对称、边界不规则、颜色斑驳和直径大于 5mm 作为黑色素瘤的特征。当皮肤病变出现这些特征时，就会被切除以进行预防。皮肤科医生可以使用一种称为皮肤镜检查的非侵入性技术，对皮肤病变进行更准确的评估，从而避免切除良性病变。然而，皮肤科医生需要在提取前对病变进行良好的皮肤镜分类。在本文中，我们提出了一种称为 LazyCL 的程序，以支持皮肤科医生评估皮肤病变的分类。我们的目标是使用 LazyCL 生成一个用于分类原位黑色素瘤的领域理论。
为了生成领域理论，LazyCL 过程使用了两种人工智能技术的组合：基于实例的推理和聚类。首先，LazyCL 随机创建聚类，然后使用一种称为懒惰描述归纳（LID）的懒惰学习方法对其进行逐个剔除。通过 LID，LazyCL 收集了为什么数据库中的病例应该属于某一类的解释。然后，对解释之间的关系进行分析，生成数据集的可理解聚类。在消除冗余和合并聚类之后，解释集被简化为描述“几乎”具有判别力的类的子集。其余的解释形成了一个初步的领域理论，这是专家进行知识发现的基础。
我们进行了两种实验。第一种实验是在包含 76 个黑色素瘤描述的数据库上使用 LazyCL。从这些实验中获得的领域理论与使用称为自组织映射（SOM）的不同聚类方法进行的先前实验进行了比较。LazyCL 和 SOM 两种方法的结果相似。第二种实验是在来自欧文大学机器学习知识库的知名领域使用 LazyCL。因此，由于这些领域具有已知的解决方案类，我们可以证明由 LazyCL 构建的聚类是正确的。
我们可以得出结论，使用基于解释的案例推理进行知识发现的 LazyCL 可用于构建领域理论。一方面，在黑色素瘤数据库上的实验表明，由 LazyCL 构建的领域理论易于理解。LID 提供的解释很容易被领域专家理解，因为这些描述涉及他们用来表示领域对象的相同属性。另一方面，在标准机器学习数据集上的实验表明，LazyCL 是一种很好的聚类方法，因为生成的所有聚类都是正确的。
